Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 20 of 30 for “"Statistical Learning Theory"”.
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Application of statistical learning theory to plankton image analysis
… This thesis addresses the problem by applying statistical machine learning to video images collected by an optical sampler, the Video Plankton Recorder (VPR). The research is focused on development of a real-time automatic plankton recognition system to estimate plankton abundance. The system …
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Application of statistical learning theory to plankton image analysis
… This thesis addresses the problem by applying statistical machine learning to video images collected by an optical sampler, the Video Plankton Recorder (VPR). The research is focused on development of a real-time automatic plankton recognition system to estimate plankton abundance. The system …
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Land cover mapping through optimizing remote sensing data for SVM classification
… classification technique that has its roots in statistical learning theory. It has gained popularity in fields such as machine vision, artificial intelligence, digital image processing and more recently remote sensing. The three commonly used SVMs include linear, polynomial and radial basis …
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Evolutionary Optimization Of Support Vector Machines
… have become increasingly popular in the machine learning community. They present several advantages over other methods like neural networks in areas like training speed, convergence, complexity control of the classifier, as well as a stronger mathematical background based on optimization and …
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Frugal hypothesis testing and classification
… and analysis of decision rules using detection theory and statistical learning theory is important because decision making under uncertainty is pervasive. Three perspectives on limiting the complexity of decision rules are considered in this thesis: geometric regularization, dimensionality …
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Computationally Intensive Methods for Spectrum Estimation
… address these problems by using techniques from statistical learning theory. This thesis presents three theoretical contributions for improving methods related to the multitaper spectrum estimation method: (1) two hypothesis testing procedures for evaluating the choice of time-bandwidth, NW, and …
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Breaking and Building Encrypted Databases
… process I develop new technical tools based on statistical learning theory. Finally, informed by an understanding of existing databases, I propose a novel performance-security tradeoff for encrypted key-value stores. I instantiate that new tradeoff with frequency smoothing, analyze it using new …
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A Systems Theoretic Framework for Online Machine Learning with an Empirical Application
Online (machine) learning is an active field of research which has been widely explored in terms of statistical learning theory, convex optimization theory and game theory, however, little to no frameworks exist for the design and application of online learning systems, both in theory and in …
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SVM-based Strategies as applied to Electromagnetics
… of the electromagnetic approaches based on learning-by-example (LBE) techniques, this thesis focuses on the development of a strategy for the solution of complex problems by means of support vector machine (SVM). The proposed instance-based classification method compared to more traditional …
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Hierarchical learning : theory with applications in speech and vision
Over the past two decades several hierarchical learning models have been developed and applied to a diverse range of practical tasks with much success. Little is known, however, as to why such models work as well as they do. Indeed, most are difficult to analyze, and cannot be easily characterized …
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Statistical methods for biomedical signal analysis and processing
Statistical modelling and statistical learning theory are two powerful analytical frameworks for analyzing signals and developing efficient processing and classification algorithms. In this thesis, these frameworks are applied for modelling and processing biomedical signals in two different …
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Robustness and generalization guarantees for statistical learning of generative models
We apply tools from the classical statistical learning theory to analyze theoretical properties of modern machine learning problems that are typically phrased in the context of generative models. By combining standard methods based on the theory of empirical processes with ideas from optimal …
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Computational studies of hydrogen storage materials and the development of related methods
… methods, including density functional theory and the cluster expansion formalism, are used to study materials for hydrogen storage. The storage of molecular hydrogen in the metal-organic framework with formula unit Zn40(02C-C6H6-COD3 is considered. It is predicted that hydrogen adsorbs …
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Animal Motion Analysis and Approximation for Robotics
… multiple areas, such as biomechanics, control theory, and machine learning, have spent their energy and efforts making robots more realistic. The intent is that the automatic system can replace real animals and even perform certain tasks in harsh, or even dangerous environments. However, animal …
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Statistical inference from dependent data : networks and Markov chains
… Computer Science, Statistics and Machine Learning. Very often, due to the process according to which data is collected, the samples in a dataset have implicit correlations amongst them. Such correlations are commonly ignored as a first approximation when trying to analyze statistical and …
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Horizons of Artificial Intelligence in Quantum Computation
… of deep neural networks in classical machine learning, a prevailing hope is that such success will translate to so-called quantum variational algorithms or quantum neural networks inspired by their classical counterparts. Contemporary deep learning algorithms are primarily developed using a …
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Modeling and Estimation of Motion Over Manifolds with Motion Capture Data
… data. This study also expands on the so-called learning problem from statistical learning theory over Euclidean spaces to estimating functions over manifolds. Experimental results are presented for estimating reptilian motion using motion capture data. The second study in this dissertation …
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Principal curves : learning, design, and applications
The subjects of this thesis are unsupervised learning in general, and principal curves in particular. Principal curves were originally defined by Hastie [Has84] and Hastie and Stuetzle [HS89] (hereafter HS) to formally capture the notion of a smooth curve passing through the "middle" of a d …
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Compound-Gaussian-regularized inverse problems: theory, algorithms, and neural networks
… algorithm unrolling to combine a powerful statistical prior, the compound Gaussian (CG) prior, with the powerful representational ability of machine learning and DNN approaches. Specifically, first we construct a novel iterative CG-regularized least squares algorithm for signal …
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LEARNING, FORECASTING AND CAUSATION AS COMPRESSION.
… tasks have been obtained by adopting Machine Learning and Pattern Recognition techniques. While a detailed analysis of these protocols' performances is beyond the scope of this discussion, some methodological aspects can be considered on a conceptual level, keeping in the background the …
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