Current Search: Georgiopoulos, Michael (x)
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- Title
- MODIFICATIONS TO THE FUZZY-ARTMAP ALGORITHM FOR DISTRIBUTED LEARNING IN LARGE DATA SETS.
- Creator
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Castro, Jose R, Georgiopoulos, Michael, University of Central Florida
- Abstract / Description
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The Fuzzy-ARTMAP (FAM) algorithm is one of the premier neural network architectures for classification problems. FAM can learn on line and is usually faster than other neural network approaches. Nevertheless the learning time of FAM can slow down considerably when the size of the training set increases into the hundreds of thousands. We apply data partitioning and networkpartitioning to the FAM algorithm in a sequential and parallel settingto achieve better convergence time and to efficiently...
Show moreThe Fuzzy-ARTMAP (FAM) algorithm is one of the premier neural network architectures for classification problems. FAM can learn on line and is usually faster than other neural network approaches. Nevertheless the learning time of FAM can slow down considerably when the size of the training set increases into the hundreds of thousands. We apply data partitioning and networkpartitioning to the FAM algorithm in a sequential and parallel settingto achieve better convergence time and to efficiently train withlarge databases (hundreds of thousands of patterns).Our parallelization is implemented on a Beowulf clusters of workstations. Two data partitioning approaches and two networkpartitioning approaches are developed. Extensive testing of all the approaches is done on three large datasets (half a milliondata points). One of them is the Forest Covertype database from Blackard and the other two are artificially generated Gaussian data with different percentages of overlap between classes.Speedups in the data partitioning approach reached the order of the hundreds without having to invest in parallel computation. Speedups onthe network partitioning approach are close to linear on a cluster of workstations. Both methods allowed us to reduce the computation time of training the neural network in large databases from days to minutes. We prove formally that the workload balance of our network partitioning approaches will never be worse than an acceptable bound, and also demonstrate the correctness of these parallelization variants of FAM.
Show less - Date Issued
- 2004
- Identifier
- CFE0000065, ucf:46092
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0000065
- Title
- GAUSS-NEWTON BASED LEARNING FOR FULLY RECURRENT NEURAL NETWORKS.
- Creator
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Vartak, Aniket Arun, Georgiopoulos, Michael, University of Central Florida
- Abstract / Description
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The thesis discusses a novel off-line and on-line learning approach for Fully Recurrent Neural Networks (FRNNs). The most popular algorithm for training FRNNs, the Real Time Recurrent Learning (RTRL) algorithm, employs the gradient descent technique for finding the optimum weight vectors in the recurrent neural network. Within the framework of the research presented, a new off-line and on-line variation of RTRL is presented, that is based on the Gauss-Newton method. The method itself is an...
Show moreThe thesis discusses a novel off-line and on-line learning approach for Fully Recurrent Neural Networks (FRNNs). The most popular algorithm for training FRNNs, the Real Time Recurrent Learning (RTRL) algorithm, employs the gradient descent technique for finding the optimum weight vectors in the recurrent neural network. Within the framework of the research presented, a new off-line and on-line variation of RTRL is presented, that is based on the Gauss-Newton method. The method itself is an approximate Newton's method tailored to the specific optimization problem, (non-linear least squares), which aims to speed up the process of FRNN training. The new approach stands as a robust and effective compromise between the original gradient-based RTRL (low computational complexity, slow convergence) and Newton-based variants of RTRL (high computational complexity, fast convergence). By gathering information over time in order to form Gauss-Newton search vectors, the new learning algorithm, GN-RTRL, is capable of converging faster to a better quality solution than the original algorithm. Experimental results reflect these qualities of GN-RTRL, as well as the fact that GN-RTRL may have in practice lower computational cost in comparison, again, to the original RTRL.
Show less - Date Issued
- 2004
- Identifier
- CFE0000091, ucf:46065
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0000091
- Title
- OPTIMIZATION OF NETWORK PARAMETERS AND SEMI-SUPERVISION IN GAUSSIAN ART ARCHITECTURES.
- Creator
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Chalasani, Roopa, Georgiopoulos, Michael, University of Central Florida
- Abstract / Description
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In this thesis we extensively experiment with two ART (adaptive resonance theory) architectures called Gaussian ARTMAP (GAM) and Distributed Gaussian ARTMAP (dGAM). Both of these classifiers have been successfully used in the past on a variety of applications. One of our contributions in this thesis is extensively experiments with the GAM and dGAM network parameters and appropriately identifying ranges for these parameters for which these architectures attain good performance (good...
Show moreIn this thesis we extensively experiment with two ART (adaptive resonance theory) architectures called Gaussian ARTMAP (GAM) and Distributed Gaussian ARTMAP (dGAM). Both of these classifiers have been successfully used in the past on a variety of applications. One of our contributions in this thesis is extensively experiments with the GAM and dGAM network parameters and appropriately identifying ranges for these parameters for which these architectures attain good performance (good classification performance and small network size). Furthermore, we have implemented novel modifications of these architectures, called semi-supervised GAM and dGAM architectures. Semi-supervision is a concept that has been used effectively before with the FAM and EAM architectures and in this thesis we are answering the question of whether semi-supervision has the same beneficial effect on the GAM architectures too. Finally, we compared the performance of GAM, dGAM, EAM, FAM and their semi-supervised versions on a number of datasets (simulated and real datasets). These experiments allowed us to draw appropriate conclusions regarding the comparative performance of these architectures.
Show less - Date Issued
- 2005
- Identifier
- CFE0000474, ucf:46373
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0000474
- Title
- AN ANALYSIS OF MISCLASSIFICATION RATES FOR DECISION TREES.
- Creator
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Zhong, Mingyu, Georgiopoulos, Michael, University of Central Florida
- Abstract / Description
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The decision tree is a well-known methodology for classification and regression. In this dissertation, we focus on the minimization of the misclassification rate for decision tree classifiers. We derive the necessary equations that provide the optimal tree prediction, the estimated risk of the tree's prediction, and the reliability of the tree's risk estimation. We carry out an extensive analysis of the application of Lidstone's law of succession for the estimation of the class...
Show moreThe decision tree is a well-known methodology for classification and regression. In this dissertation, we focus on the minimization of the misclassification rate for decision tree classifiers. We derive the necessary equations that provide the optimal tree prediction, the estimated risk of the tree's prediction, and the reliability of the tree's risk estimation. We carry out an extensive analysis of the application of Lidstone's law of succession for the estimation of the class probabilities. In contrast to existing research, we not only compute the expected values of the risks but also calculate the corresponding reliability of the risk (measured by standard deviations). We also provide an explicit expression of the k-norm estimation for the tree's misclassification rate that combines both the expected value and the reliability. Furthermore, our proposed and proven theorem on k-norm estimation suggests an efficient pruning algorithm that has a clear theoretical interpretation, is easily implemented, and does not require a validation set. Our experiments show that our proposed pruning algorithm produces accurate trees quickly that compares very favorably with two other well-known pruning algorithms, CCP of CART and EBP of C4.5. Finally, our work provides a deeper understanding of decision trees.
Show less - Date Issued
- 2007
- Identifier
- CFE0001774, ucf:47271
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0001774
- Title
- GENETICALLY ENGINEERED ADAPTIVE RESONANCE THEORY (ART) NEURAL NETWORK ARCHITECTURES.
- Creator
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Al-Daraiseh, Ahmad, Georgiopoulos, Michael, University of Central Florida
- Abstract / Description
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Fuzzy ARTMAP (FAM) is currently considered to be one of the premier neural network architectures in solving classification problems. One of the limitations of Fuzzy ARTMAP that has been extensively reported in the literature is the category proliferation problem. That is Fuzzy ARTMAP has the tendency of increasing its network size, as it is confronted with more and more data, especially if the data is of noisy and/or overlapping nature. To remedy this problem a number of researchers have...
Show moreFuzzy ARTMAP (FAM) is currently considered to be one of the premier neural network architectures in solving classification problems. One of the limitations of Fuzzy ARTMAP that has been extensively reported in the literature is the category proliferation problem. That is Fuzzy ARTMAP has the tendency of increasing its network size, as it is confronted with more and more data, especially if the data is of noisy and/or overlapping nature. To remedy this problem a number of researchers have designed modifications to the training phase of Fuzzy ARTMAP that had the beneficial effect of reducing this phenomenon. In this thesis we propose a new approach to handle the category proliferation problem in Fuzzy ARTMAP by evolving trained FAM architectures. We refer to the resulting FAM architectures as GFAM. We demonstrate through extensive experimentation that an evolved FAM (GFAM) exhibits good (sometimes optimal) generalization, small size (sometimes optimal size), and requires reasonable computational effort to produce an optimal or sub-optimal network. Furthermore, comparisons of the GFAM with other approaches, proposed in the literature, which address the FAM category proliferation problem, illustrate that the GFAM has a number of advantages (i.e. produces smaller or equal size architectures, of better or as good generalization, with reduced computational complexity). Furthermore, in this dissertation we have extended the approach used with Fuzzy ARTMAP to other ART architectures, such as Ellipsoidal ARTMAP (EAM) and Gaussian ARTMAP (GAM) that also suffer from the ART category proliferation problem. Thus, we have designed and experimented with genetically engineered EAM and GAM architectures, named GEAM and GGAM. Comparisons of GEAM and GGAM with other ART architectures that were introduced in the ART literature, addressing the category proliferation problem, illustrate similar advantages observed by GFAM (i.e, GEAM and GGAM produce smaller size ART architectures, of better or improved generalization, with reduced computational complexity). Moverover, to optimally cover the input space of a problem, we proposed a genetically engineered ART architecture that combines the category structures of two different ART networks, FAM and EAM. We named this architecture UART (Universal ART). We analyzed the order of search in UART, that is the order according to which a FAM category or an EAM category is accessed in UART. This analysis allowed us to better understand UART's functionality. Experiments were also conducted to compare UART with other ART architectures, in a similar fashion as GFAM and GEAM were compared. Similar conclusions were drawn from this comparison, as in the comparison of GFAM and GEAM with other ART architectures. Finally, we analyzed the computational complexity of the genetically engineered ART architectures and we compared it with the computational complexity of other ART architectures, introduced into the literature. This analytical comparison verified our claim that the genetically engineered ART architectures produce better generalization and smaller sizes ART structures, at reduced computational complexity, compared to other ART approaches. In review, a methodology was introduced of how to combine the answers (categories) of ART architectures, using genetic algorithms. This methodology was successfully applied to FAM, EAM and FAM and EAM ART architectures, with success, resulting in ART neural networks which outperformed other ART architectures, previously introduced into the literature, and quite often produced ART architectures that attained optimal classification results, at reduced computational complexity.
Show less - Date Issued
- 2006
- Identifier
- CFE0000977, ucf:46696
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0000977
- Title
- AN ARCHITECTURE FOR HIGH-PERFORMANCE PRIVACY-PRESERVING AND DISTRIBUTED DATA MINING.
- Creator
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Secretan, James, Georgiopoulos, Michael, University of Central Florida
- Abstract / Description
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This dissertation discusses the development of an architecture and associated techniques to support Privacy Preserving and Distributed Data Mining. The field of Distributed Data Mining (DDM) attempts to solve the challenges inherent in coordinating data mining tasks with databases that are geographically distributed, through the application of parallel algorithms and grid computing concepts. The closely related field of Privacy Preserving Data Mining (PPDM) adds the dimension of privacy to...
Show moreThis dissertation discusses the development of an architecture and associated techniques to support Privacy Preserving and Distributed Data Mining. The field of Distributed Data Mining (DDM) attempts to solve the challenges inherent in coordinating data mining tasks with databases that are geographically distributed, through the application of parallel algorithms and grid computing concepts. The closely related field of Privacy Preserving Data Mining (PPDM) adds the dimension of privacy to the problem, trying to find ways that organizations can collaborate to mine their databases collectively, while at the same time preserving the privacy of their records. Developing data mining algorithms for DDM and PPDM environments can be difficult and there is little software to support it. In addition, because these tasks can be computationally demanding, taking hours of even days to complete data mining tasks, organizations should be able to take advantage of high-performance and parallel computing to accelerate these tasks. Unfortunately there is no such framework that is able to provide all of these services easily for a developer. In this dissertation such a framework is developed to support the creation and execution of DDM and PPDM applications, called APHID (Architecture for Private, High-performance Integrated Data mining). The architecture allows users to flexibly and seamlessly integrate cluster and grid resources into their DDM and PPDM applications. The architecture is scalable, and is split into highly de-coupled services to ensure flexibility and extensibility. This dissertation first develops a comprehensive example algorithm, a privacy-preserving Probabilistic Neural Network (PNN), which serves a basis for analysis of the difficulties of DDM/PPDM development. The privacy-preserving PNN is the first such PNN in the literature, and provides not only a practical algorithm ready for use in privacy-preserving applications, but also a template for other data intensive algorithms, and a starting point for analyzing APHID's architectural needs. After analyzing the difficulties in the PNN algorithm's development, as well as the shortcomings of researched systems, this dissertation presents the first concrete programming model joining high performance computing resources with a privacy preserving data mining process. Unlike many of the existing PPDM development models, the platform of services is language independent, allowing layers and algorithms to be implemented in popular languages (Java, C++, Python, etc.). An implementation of a PPDM algorithm is developed in Java utilizing the new framework. Performance results are presented, showing that APHID can enable highly simplified PPDM development while speeding up resource intensive parts of the algorithm.
Show less - Date Issued
- 2009
- Identifier
- CFE0002853, ucf:48076
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0002853
- Title
- AN ADAPTIVE MULTIOBJECTIVE EVOLUTIONARY APPROACH TO OPTIMIZE ARTMAP NEURAL NETWORKS.
- Creator
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Kaylani, Assem, Georgiopoulos, Michael, University of Central Florida
- Abstract / Description
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This dissertation deals with the evolutionary optimization of ART neural network architectures. ART (adaptive resonance theory) was introduced by a Grossberg in 1976. In the last 20 years (1987-2007) a number of ART neural network architectures were introduced into the literature (Fuzzy ARTMAP (1992), Gaussian ARTMAP (1996 and 1997) and Ellipsoidal ARTMAP (2001)). In this dissertation, we focus on the evolutionary optimization of ART neural network architectures with the intent of optimizing...
Show moreThis dissertation deals with the evolutionary optimization of ART neural network architectures. ART (adaptive resonance theory) was introduced by a Grossberg in 1976. In the last 20 years (1987-2007) a number of ART neural network architectures were introduced into the literature (Fuzzy ARTMAP (1992), Gaussian ARTMAP (1996 and 1997) and Ellipsoidal ARTMAP (2001)). In this dissertation, we focus on the evolutionary optimization of ART neural network architectures with the intent of optimizing the size and the generalization performance of the ART neural network. A number of researchers have focused on the evolutionary optimization of neural networks, but no research has been performed on the evolutionary optimization of ART neural networks, prior to 2006, when Daraiseh has used evolutionary techniques for the optimization of ART structures. This dissertation extends in many ways and expands in different directions the evolution of ART architectures, such as: (a) uses a multi-objective optimization of ART structures, thus providing to the user multiple solutions (ART networks) with varying degrees of merit, instead of a single solution (b) uses GA parameters that are adaptively determined throughout the ART evolution, (c) identifies a proper size of the validation set used to calculate the fitness function needed for ART's evolution, thus speeding up the evolutionary process, (d) produces experimental results that demonstrate the evolved ART's effectiveness (good accuracy and small size) and efficiency (speed) compared with other competitive ART structures, as well as other classifiers (CART (Classification and Regression Trees) and SVM (Support Vector Machines)). The overall methodology to evolve ART using a multi-objective approach, the chromosome representation of an ART neural network, the genetic operators used in ART's evolution, and the automatic adaptation of some of the GA parameters in ART's evolution could also be applied in the evolution of other exemplar based neural network classifiers such as the probabilistic neural network and the radial basis function neural network.
Show less - Date Issued
- 2008
- Identifier
- CFE0002212, ucf:47907
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0002212
- Title
- SCALABLE AND EFFICIENT OUTLIER DETECTION IN LARGE DISTRIBUTED DATA SETS WITH MIXED-TYPE ATTRIBUTES.
- Creator
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Koufakou, Anna, Georgiopoulos, Michael, University of Central Florida
- Abstract / Description
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An important problem that appears often when analyzing data involves identifying irregular or abnormal data points called outliers. This problem broadly arises under two scenarios: when outliers are to be removed from the data before analysis, and when useful information or knowledge can be extracted by the outliers themselves. Outlier Detection in the context of the second scenario is a research field that has attracted significant attention in a broad range of useful applications. For...
Show moreAn important problem that appears often when analyzing data involves identifying irregular or abnormal data points called outliers. This problem broadly arises under two scenarios: when outliers are to be removed from the data before analysis, and when useful information or knowledge can be extracted by the outliers themselves. Outlier Detection in the context of the second scenario is a research field that has attracted significant attention in a broad range of useful applications. For example, in credit card transaction data, outliers might indicate potential fraud; in network traffic data, outliers might represent potential intrusion attempts. The basis of deciding if a data point is an outlier is often some measure or notion of dissimilarity between the data point under consideration and the rest. Traditional outlier detection methods assume numerical or ordinal data, and compute pair-wise distances between data points. However, the notion of distance or similarity for categorical data is more difficult to define. Moreover, the size of currently available data sets dictates the need for fast and scalable outlier detection methods, thus precluding distance computations. Additionally, these methods must be applicable to data which might be distributed among different locations. In this work, we propose novel strategies to efficiently deal with large distributed data containing mixed-type attributes. Specifically, we first propose a fast and scalable algorithm for categorical data (AVF), and its parallel version based on MapReduce (MR-AVF). We extend AVF and introduce a fast outlier detection algorithm for large distributed data with mixed-type attributes (ODMAD). Finally, we modify ODMAD in order to deal with very high-dimensional categorical data. Experiments with large real-world and synthetic data show that the proposed methods exhibit large performance gains and high scalability compared to the state-of-the-art, while achieving similar accuracy detection rates.
Show less - Date Issued
- 2009
- Identifier
- CFE0002734, ucf:48161
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0002734
- Title
- Effective Task Transfer Through Indirect Encoding.
- Creator
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Verbancsics, Phillip, Stanley, Kenneth, Sukthankar, Gita, Georgiopoulos, Michael, Garibay, Ivan, University of Central Florida
- Abstract / Description
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An important goal for machine learning is to transfer knowledge between tasks. For example, learning to play RoboCup Keepaway should contribute to learning the full game of RoboCup soccer. Often approaches to task transfer focus on transforming the original representation to fit the new task. Such representational transformations are necessary because the target task often requires new state information that was not included in the original representation. In RoboCup Keepaway, changing from...
Show moreAn important goal for machine learning is to transfer knowledge between tasks. For example, learning to play RoboCup Keepaway should contribute to learning the full game of RoboCup soccer. Often approaches to task transfer focus on transforming the original representation to fit the new task. Such representational transformations are necessary because the target task often requires new state information that was not included in the original representation. In RoboCup Keepaway, changing from the 3 vs. 2 variant of the task to 4 vs. 3 adds state information for each of the new players. In contrast, this dissertation explores the idea that transfer is most effective if the representation is designed to be the same even across different tasks. To this end, (1) the bird's eye view (BEV) representation is introduced, which can represent different tasks on the same two-dimensional map. Because the BEV represents state information associated with positions instead of objects, it can be scaled to more objects without manipulation. In this way, both the 3 vs. 2 and 4 vs. 3 Keepaway tasks can be represented on the same BEV, which is (2) demonstrated in this dissertation.Yet a challenge for such representation is that a raw two-dimensional map is high-dimensional and unstructured. This dissertation demonstrates how this problem is addressed naturally by the Hypercube-based NeuroEvolution of Augmenting Topologies (HyperNEAT) approach. HyperNEAT evolves an indirect encoding, which compresses the representation by exploiting its geometry. The dissertation then explores further exploiting the power of such encoding, beginning by (3) enhancing the configuration of the BEV with a focus on modularity. The need for further nonlinearity is then (4) investigated through the addition of hidden nodes. Furthermore, (5) the size of the BEV can be manipulated because it is indirectly encoded. Thus the resolution of the BEV, which is dictated by its size, is increased in precision and culminates in a HyperNEAT extension that is expressed at effectively infinite resolution. Additionally, scaling to higher resolutions through gradually increasing the size of the BEV is explored. Finally, (6) the ambitious problem of scaling from the Keepaway task to the Half-field Offense task is investigated with the BEV. Overall, this dissertation demonstrates that advanced representations in conjunction with indirect encoding can contribute to scaling learning techniques to more challenging tasks, such as the Half-field Offense RoboCup soccer domain.
Show less - Date Issued
- 2011
- Identifier
- CFE0004174, ucf:49071
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0004174
- Title
- Brightness Temperature Calibration of SAC-D/Aquarius Microwave Radiometer (MWR).
- Creator
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Biswas, Sayak, Jones, W, Georgiopoulos, Michael, Wahid, Parveen, Wilheit, Thomas, University of Central Florida
- Abstract / Description
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The Aquarius/SAC-D joint international science mission, between the NationalAeronautics and Space Administration (NASA) of United States and the Argentine Space Agency (Comision Nacional de Actividades Espaciales, CONAE), was launched on a polar-orbiting satellite on June 10, 2011. This mission of discovery will provide measurements of the global sea surface salinity, which contributes to understanding climatic changes in the global water cycle and how these variations inuence the general...
Show moreThe Aquarius/SAC-D joint international science mission, between the NationalAeronautics and Space Administration (NASA) of United States and the Argentine Space Agency (Comision Nacional de Actividades Espaciales, CONAE), was launched on a polar-orbiting satellite on June 10, 2011. This mission of discovery will provide measurements of the global sea surface salinity, which contributes to understanding climatic changes in the global water cycle and how these variations inuence the general ocean circulation. The Microwave Radiometer (MWR), a three channel Dicke radiometer operating at 23.8 GHz H-Pol and 36.5 GHz V-(&) H-Pol provided by CONAE, will complement Aquarius (NASA's L-band radiometer/scatterometer) by providing simultaneous spatially collocated environmental measurements such as water vapor, cloud liquid water, surface wind speed, rain rate and sea ice concentration.This dissertation focuses on the overall radiometric calibration of MWR instrument.Which means establishing a transfer function that relates the instrument output to the antenna brightness temperature (Tb). To achieve this goal, the dissertation describes a microwave radiative transfer model of the instrument and validates it using the laboratory and thermal-vacuum test data. This involves estimation of the losses and physical temperature profile in the path from the receiver to each antenna feed-horn for all the receivers. As the pre-launch laboratory tests can only provide a simulated environment which is very different from the operational environment in space, an on-orbit calibration of the instrument is very important. Inter-satellite radiometric cross-calibration of MWR using the Naval Research Laboratory's multi-frequency polarimetric microwave radiometer, WindSat, on board the Coriolis satellite is also an important part of this dissertation. Cross-calibration between two different satellite instruments require normalization of Tb's to account for the frequency and incidence angle dierence between the instruments. Also inter-satellite calibration helps to determine accurate antenna pattern correction coefficients and other small instrument biases.
Show less - Date Issued
- 2012
- Identifier
- CFE0004200, ucf:49033
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0004200
- Title
- Life Long Learning in Sparse Learning Environments.
- Creator
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Reeder, John, Georgiopoulos, Michael, Gonzalez, Avelino, Sukthankar, Gita, Anagnostopoulos, Georgios, University of Central Florida
- Abstract / Description
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Life long learning is a machine learning technique that deals with learning sequential tasks over time. It seeks to transfer knowledge from previous learning tasks to new learning tasks in order to increase generalization performance and learning speed. Real-time learning environments in which many agents are participating may provide learning opportunities but they are spread out in time and space outside of the geographical scope of a single learning agent. This research seeks to provide an...
Show moreLife long learning is a machine learning technique that deals with learning sequential tasks over time. It seeks to transfer knowledge from previous learning tasks to new learning tasks in order to increase generalization performance and learning speed. Real-time learning environments in which many agents are participating may provide learning opportunities but they are spread out in time and space outside of the geographical scope of a single learning agent. This research seeks to provide an algorithm and framework for life long learning among a network of agents in a sparse real-time learning environment. This work will utilize the robust knowledge representation of neural networks, and make use of both functional and representational knowledge transfer to accomplish this task. A new generative life long learning algorithm utilizing cascade correlation and reverberating pseudo-rehearsal and incorporating a method for merging divergent life long learning paths will be implemented.
Show less - Date Issued
- 2013
- Identifier
- CFE0004917, ucf:49601
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0004917
- Title
- Active Learning with Unreliable Annotations.
- Creator
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Zhao, Liyue, Sukthankar, Gita, Tappen, Marshall, Georgiopoulos, Michael, Sukthankar, Rahul, University of Central Florida
- Abstract / Description
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With the proliferation of social media, gathering data has became cheaper and easier than before. However, this data can not be used for supervised machine learning without labels. Asking experts to annotate sufficient data for training is both expensive and time-consuming. Current techniques provide two solutions to reducing the cost and providing sufficient labels: crowdsourcing and active learning. Crowdsourcing, which outsources tasks to a distributed group of people, can be used to...
Show moreWith the proliferation of social media, gathering data has became cheaper and easier than before. However, this data can not be used for supervised machine learning without labels. Asking experts to annotate sufficient data for training is both expensive and time-consuming. Current techniques provide two solutions to reducing the cost and providing sufficient labels: crowdsourcing and active learning. Crowdsourcing, which outsources tasks to a distributed group of people, can be used to provide a large quantity of labels but controlling the quality of labels is hard. Active learning, which requires experts to annotate a subset of the most informative or uncertain data, is very sensitive to the annotation errors. Though these two techniques can be used independently of one another, by using them in combination they can complement each other's weakness. In this thesis, I investigate the development of active learning Support Vector Machines (SVMs) and expand this model to sequential data. Then I discuss the weakness of combining active learning and crowdsourcing, since the active learning is very sensitive to low quality annotations which are unavoidable for labels collected from crowdsourcing. In this thesis, I propose three possible strategies, incremental relabeling, importance-weighted label prediction and active Bayesian Networks. The incremental relabeling strategy requires workers to devote more annotations to uncertain samples, compared to majority voting which allocates different samples the same number of labels. Importance-weighted label prediction employs an ensemble of classifiers to guide the label requests from a pool of unlabeled training data. An active learning version of Bayesian Networks is used to model the difficulty of samples and the expertise of workers simultaneously to evaluate the relative weight of workers' labels during the active learning process. All three strategies apply different techniques with the same expectation -- identifying the optimal solution for applying an active learning model with mixed label quality to crowdsourced data. However, the active Bayesian Networks model, which is the core element of this thesis, provides additional benefits by estimating the expertise of workers during the training phase. As an example application, I also demonstrate the utility of crowdsourcing for human activity recognition problems.
Show less - Date Issued
- 2013
- Identifier
- CFE0004965, ucf:49579
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0004965
- Title
- Detecting, Tracking, and Recognizing Activities in Aerial Video.
- Creator
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Reilly, Vladimir, Shah, Mubarak, Georgiopoulos, Michael, Stanley, Kenneth, Dogariu, Aristide, University of Central Florida
- Abstract / Description
-
In this dissertation we address the problem of detecting humans and vehicles, tracking their identities in crowded scenes, and finally determining human activities. First, we tackle the problem of detecting moving as well as stationary objects in scenes that contain parallax and shadows. We constrain the search of pedestrians and vehicles by representing them as shadow casting out of plane or (SCOOP) objects.Next, we propose a novel method for tracking a large number of densely moving objects...
Show moreIn this dissertation we address the problem of detecting humans and vehicles, tracking their identities in crowded scenes, and finally determining human activities. First, we tackle the problem of detecting moving as well as stationary objects in scenes that contain parallax and shadows. We constrain the search of pedestrians and vehicles by representing them as shadow casting out of plane or (SCOOP) objects.Next, we propose a novel method for tracking a large number of densely moving objects in aerial video. We divide the scene into grid cells to define a set of local scene constraints which we use as part of the matching cost function to solve the tracking problem which allows us to track fast-moving objects in low frame rate videos.Finally, we propose a method for recognizing human actions from few examples. We use the bag of words action representation, assume that most of the classes have many examples, and construct Support Vector Machine models for each class. We then use Support Vector Machines for classes with many examples to improve the decision function of the Support Vector Machine that was trained using few examples via late fusion of weighted decision values.
Show less - Date Issued
- 2012
- Identifier
- CFE0004627, ucf:49935
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0004627
- Title
- Investigators' Perceptions of Inter-Jurisdictional Law Enforcement Information Sharing On Criminal Investigative Success: An Exploratory Analysis.
- Creator
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Freeman, Jennifer, Reynolds, Kenneth, Winton, Mark, Ford, Robert, Georgiopoulos, Michael, University of Central Florida
- Abstract / Description
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Information sharing among law enforcement entities became a national priority after the 9/11 attack (Carter, 2005). Various information systems utilized by law enforcement agencies may be promising; however, there is little extant empirical research to validate the system's effectiveness related to increasing investigative success (Bureau of Justice Assistance, 2010). One information system that has tied together numerous Florida law enforcement agencies is the FINDER system. FINDER, the...
Show moreInformation sharing among law enforcement entities became a national priority after the 9/11 attack (Carter, 2005). Various information systems utilized by law enforcement agencies may be promising; however, there is little extant empirical research to validate the system's effectiveness related to increasing investigative success (Bureau of Justice Assistance, 2010). One information system that has tied together numerous Florida law enforcement agencies is the FINDER system. FINDER, the Florida Integrated Network for Data Exchange and Retrieval system, provides agency investigators a wide range of information not previously available (Reynolds, Griset, (&) Scott, 2006; Scott, 2006). This study's foundation was primarily based upon the conceptual frameworks of diffusion of innovations and knowledge management. Survey based information from investigators using FINDER and those using a non-FINDER information system was obtained and analyzed to determine if the information impacted investigative success. Questionnaires were sent to those law enforcement investigators that participate in the FINDER system, as well as those who use a non-FINDER system. Through descriptive and regression analysis, it was found that FINDER participants reported there was a positive contribution to investigative success. The research also found that certain information obtained from FINDER assisted in arrests and an investigator's ability to solve cases. This study provides a foundation for further information system research related to case solvability and investigative success.
Show less - Date Issued
- 2014
- Identifier
- CFE0005167, ucf:50660
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0005167
- Title
- Modeling Learner Mood in Realtime through Biosensors for Intelligent Tutoring Improvements.
- Creator
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Brawner, Keith, Gonzalez, Avelino, Boloni, Ladislau, Georgiopoulos, Michael, Proctor, Michael, Beidel, Deborah, University of Central Florida
- Abstract / Description
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Computer-based instructors, just like their human counterparts, should monitor the emotional and cognitive states of their students in order to adapt instructional technique. Doing so requires a model of student state to be available at run time, but this has historically been difficult. Because people are different, generalized models have not been able to be validated. As a person's cognitive and affective state vary over time of day and seasonally, individualized models have had differing...
Show moreComputer-based instructors, just like their human counterparts, should monitor the emotional and cognitive states of their students in order to adapt instructional technique. Doing so requires a model of student state to be available at run time, but this has historically been difficult. Because people are different, generalized models have not been able to be validated. As a person's cognitive and affective state vary over time of day and seasonally, individualized models have had differing difficulties. The simultaneous creation and execution of an individualized model, in real time, represents the last option for modeling such cognitive and affective states. This dissertation presents and evaluates four differing techniques for the creation of cognitive and affective models that are created on-line and in real time for each individual user as alternatives to generalized models. Each of these techniques involves making predictions and modifications to the model in real time, addressing the real time datastream problems of infinite length, detection of new concepts, and responding to how concepts change over time. Additionally, with the knowledge that a user is physically present, this work investigates the contribution that the occasional direct user query can add to the overall quality of such models. The research described in this dissertation finds that the creation of a reasonable quality affective model is possible with an infinitesimal amount of time and without (")ground truth(") knowledge of the user, which is shown across three different emotional states. Creation of a cognitive model in the same fashion, however, was not possible via direct AI modeling, even with all of the (")ground truth(") information available, which is shown across four different cognitive states.
Show less - Date Issued
- 2013
- Identifier
- CFE0004822, ucf:49734
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0004822
- Title
- Modeling and Simulation of All-electric Aircraft Power Generation and Actuation.
- Creator
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Woodburn, David, Wu, Xinzhang, Batarseh, Issa, Georgiopoulos, Michael, Haralambous, Michael, Chow, Louis, University of Central Florida
- Abstract / Description
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Modern aircraft, military and commercial, rely extensively on hydraulic systems. However, there is great interest in the avionics community to replace hydraulic systems with electric systems. There are physical challenges to replacing hydraulic actuators with electromechanical actuators (EMAs), especially for flight control surface actuation. These include dynamic heat generation and power management.Simulation is seen as a powerful tool in making the transition to all-electric aircraft by...
Show moreModern aircraft, military and commercial, rely extensively on hydraulic systems. However, there is great interest in the avionics community to replace hydraulic systems with electric systems. There are physical challenges to replacing hydraulic actuators with electromechanical actuators (EMAs), especially for flight control surface actuation. These include dynamic heat generation and power management.Simulation is seen as a powerful tool in making the transition to all-electric aircraft by predicting the dynamic heat generated and the power flow in the EMA. Chapter 2 of this dissertation describes the nonlinear, lumped-element, integrated modeling of a permanent magnet (PM) motor used in an EMA. This model is capable of representing transient dynamics of an EMA, mechanically, electrically, and thermally.Inductance is a primary parameter that links the electrical and mechanical domains and, therefore, is of critical importance to the modeling of the whole EMA. In the dynamic mode of operation of an EMA, the inductances are quite nonlinear. Chapter 3 details the careful analysis of the inductances from finite element software and the mathematical modeling of these inductances for use in the overall EMA model.Chapter 4 covers the design and verification of a nonlinear, transient simulation model of a two-step synchronous generator with three-phase rectifiers. Simulation results are shown.
Show less - Date Issued
- 2013
- Identifier
- CFE0005074, ucf:49975
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0005074
- Title
- Semiconductor Design and Manufacturing Interplay to Achieve Higher Yields at Reduced Costs using SMART Techniques.
- Creator
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Oberai, Ankush Bharati, Yuan, Jiann-Shiun, Abdolvand, Reza, Georgiopoulos, Michael, Sundaram, Kalpathy, Reilly, Charles, University of Central Florida
- Abstract / Description
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Since the outset of IC Semiconductor market there has been a gap between its design and manufacturing communities. This gap continued to grow as the device geometries started to shrink and the manufacturing processes and tools got more complex. This gap lowered the manufacturing yield, leading to higher cost of ICs and delay in their time to market. It also impacted performance of the ICs, impacting the overall functionality of the systems they were integrated in. However, in the recent years...
Show moreSince the outset of IC Semiconductor market there has been a gap between its design and manufacturing communities. This gap continued to grow as the device geometries started to shrink and the manufacturing processes and tools got more complex. This gap lowered the manufacturing yield, leading to higher cost of ICs and delay in their time to market. It also impacted performance of the ICs, impacting the overall functionality of the systems they were integrated in. However, in the recent years there have been major efforts to bridge the gap between design and manufacturing using software solutions by providing closer collaborations techniques between design and manufacturing communities. The root cause of this gap is inherited by the difference in the knowledge and skills required by the two communities. The IC design community is more microelectronics, electrical engineering and software driven whereas the IC manufacturing community is more driven by material science, mechanical engineering, physics and robotics. The cross training between the two is almost nonexistence and not even mandated. This gap is deemed to widen, with demand for more complex designs and miniaturization of electronic appliance-products. Growing need for MEMS, 3-D NANDS and IOTs are other drivers that could widen the gap between design and manufacturing. To bridge this gap, it is critical to have close loop solutions between design and manufacturing This could be achieved by SMART automation on both sides by using Artificial Intelligence, Machine Learning and Big Data algorithms. Lack of automation and predictive capabilities have even made the situation worse on the yield and total turnaround times. With the growing fabless and foundry business model, bridging the gap has become even more critical. Smart Manufacturing philosophy must be adapted to make this bridge possible. We need to understand the Fab-fabless collaboration requirements and the mechanism to bring design to the manufacturing floor for yield improvement. Additionally, design community must be educated with manufacturing process and tool knowledge, so they can design for improved manufacturability. This study will require understanding of elements impacting manufacturing on both ends of the design and manufacturing process. Additionally, we need to understand the process rules that need to be followed closely in the design phase. Best suited SMART automation techniques to bridge the gap need to be studied and analyzed for their effectiveness.
Show less - Date Issued
- 2018
- Identifier
- CFE0007351, ucf:52096
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007351
- Title
- Learning Kernel-based Approximate Isometries.
- Creator
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Sedghi, Mahlagha, Georgiopoulos, Michael, Anagnostopoulos, Georgios, Atia, George, Liu, Fei, University of Central Florida
- Abstract / Description
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The increasing availability of public datasets offers an inexperienced opportunity to conduct data-driven studies. Metric Multi-Dimensional Scaling aims to find a low-dimensional embedding of the data, preserving the pairwise dissimilarities amongst the data points in the original space. Along with the visualizability, this dimensionality reduction plays a pivotal role in analyzing and disclosing the hidden structures in the data. This work introduces Sparse Kernel-based Least Squares Multi...
Show moreThe increasing availability of public datasets offers an inexperienced opportunity to conduct data-driven studies. Metric Multi-Dimensional Scaling aims to find a low-dimensional embedding of the data, preserving the pairwise dissimilarities amongst the data points in the original space. Along with the visualizability, this dimensionality reduction plays a pivotal role in analyzing and disclosing the hidden structures in the data. This work introduces Sparse Kernel-based Least Squares Multi-Dimensional Scaling approach for exploratory data analysis and, when desirable, data visualization. We assume our embedding map belongs to a Reproducing Kernel Hilbert Space of vector-valued functions which allows for embeddings of previously unseen data. Also, given appropriate positive-definite kernel functions, it extends the applicability of our methodto non-numerical data. Furthermore, the framework employs Multiple Kernel Learning for implicitlyidentifying an effective feature map and, hence, kernel function. Finally, via the use ofsparsity-promoting regularizers, the technique is capable of embedding data on a, typically, lowerdimensionalmanifold by naturally inferring the embedding dimension from the data itself. In theprocess, key training samples are identified, whose participation in the embedding map's kernelexpansion is most influential. As we will show, such influence may be given interesting interpretations in the context of the data at hand. The resulting multi-kernel learning, non-convex framework can be effectively trained via a block coordinate descent approach, which alternates between an accelerated proximal average method-based iterative majorization for learning the kernel expansion coefficients and a simple quadratic program, which deduces the multiple-kernel learning coefficients. Experimental results showcase potential uses of the proposed framework on artificial data as well as real-world datasets, that underline the merits of our embedding framework. Our method discovers genuine hidden structure in the data, that in case of network data, matches the results of well-known Multi- level Modularity Optimization community structure detection algorithm.
Show less - Date Issued
- 2017
- Identifier
- CFE0007132, ucf:52315
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007132
- Title
- Human Group Behavior Modeling for Virtual Worlds.
- Creator
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Shah, Syed Fahad Allam, Sukthankar, Gita, Georgiopoulos, Michael, Foroosh, Hassan, Anagnostopoulos, Georgios, University of Central Florida
- Abstract / Description
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Virtual worlds and massively-multiplayer online games are rich sources of information about large-scale teams and groups, offering the tantalizing possibility of harvesting data about group formation, social networks, and network evolution. They provide new outlets for human social interaction that differ from both face-to-face interactions and non-physically-embodied social networking tools such as Facebook and Twitter. We aim to study group dynamics in these virtual worlds by collecting and...
Show moreVirtual worlds and massively-multiplayer online games are rich sources of information about large-scale teams and groups, offering the tantalizing possibility of harvesting data about group formation, social networks, and network evolution. They provide new outlets for human social interaction that differ from both face-to-face interactions and non-physically-embodied social networking tools such as Facebook and Twitter. We aim to study group dynamics in these virtual worlds by collecting and analyzing public conversational patterns of users grouped in close physical proximity. To do this, we created a set of tools for monitoring, partitioning, and analyzing unstructured conversations between changing groups of participants in Second Life, a massively multi-player online user-constructed environment that allows users to construct and inhabit their own 3D world. Although there are some cues in the dialog, determining social interactions from unstructured chat data alone is a difficult problem, since these environments lack many of the cues that facilitate natural language processing in other conversational settings and different types of social media. Public chat data often features players who speak simultaneously, use jargon and emoticons, and only erratically adhere to conversational norms.Humans are adept social animals capable of identifying friendship groups from a combination of linguistic cues and social network patterns. But what is more important, the content of what people say or their history of social interactions? Moreover, is it possible to identify whether people are part of a group with changing membership merely from general network properties, such as measures of centrality and latent communities? These are the questions that we aim to answer in this thesis. The contributions of this thesis include: 1) a link prediction algorithm for identifying friendship relationships from unstructured chat data 2) a method for identifying social groups based on the results of community detection and topic analysis.The output of these two algorithms (links and group membership) are useful for studying a variety of research questions about human behavior in virtual worlds. To demonstrate this we have performed a longitudinal analysis of human groups in different regions of the Second Life virtual world. We believe that studies performed with our tools in virtual worlds will be a useful stepping stone toward creating a rich computational model of human group dynamics.
Show less - Date Issued
- 2011
- Identifier
- CFE0004164, ucf:49074
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0004164
- Title
- An Optimization of Thermodynamic Efficiency vs. Capacity for Communications Systems.
- Creator
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Rawlins, Gregory, Wocjan, Pawel, Wahid, Parveen, Georgiopoulos, Michael, Jones, W Linwood, Mucciolo, Eduardo, University of Central Florida
- Abstract / Description
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This work provides a fundamental view of the mechanisms which affect the power efficiency of communications processes along with a method for efficiency enhancement. Shannon's work is the definitive source for analyzing information capacity of a communications system but his formulation does not predict an efficiency relationship suitable for calculating the power consumption of a system, particularly for practical signals which may only approach the capacity limit. This work leverages...
Show moreThis work provides a fundamental view of the mechanisms which affect the power efficiency of communications processes along with a method for efficiency enhancement. Shannon's work is the definitive source for analyzing information capacity of a communications system but his formulation does not predict an efficiency relationship suitable for calculating the power consumption of a system, particularly for practical signals which may only approach the capacity limit. This work leverages Shannon's while providing additional insight through physical models which enable the calculation and improvement of efficiency for the encoding of signals. The proliferation of Mobile Communications platforms is challenging capacity of networks largely because of the ever increasing data rate at each node. This places significant power management demands on personal computing devices as well as cellular and WLAN terminals. The increased data throughput translates to shorter meantime between battery charging cycles and increased thermal footprint. Solutions are developed herein to counter this trend. Hardware was constructed to measure the efficiency of a prototypical Gaussian signal prior to efficiency enhancement. After an optimization was performed, the efficiency of the encoding apparatus increased from 3.125% to greater than 86% for a manageable investment of resources. Likewise several telecommunications standards based waveforms were also tested on the same hardware. The results reveal that the developed physical theories extrapolate in a very accurate manner to an electronics application, predicting the efficiency of single ended and differential encoding circuits before and after optimization.
Show less - Date Issued
- 2015
- Identifier
- CFE0006051, ucf:50994
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0006051
