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.
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Showing 1 to 20 of 38 for “"statistical machine learning"”.
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Contributions to statistical machine learning algorithm
… journal papers are written as contributions to statistical machine learning algorithm literature.
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Wafer defect prediction with statistical machine learning
… The primary goal of the project is to build a statistical prediction model to facilitate operational improvements across two global manufacturing locations. The scope of the project includes one high-volume product line, an off-line statistical model using historical production data, and …
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Statistical Machine Learning for Multi-platform Biomedical Data Analysis
… data stimulates various novel applications of statistical machine learning methods in many areas of biomedical research. The main objective is to assist biomedical investigators to better interpret, analyze, and understand the biomedical questions based on the acquired data. Given the …
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Automatic Chest X-rays Analysis using Statistical Machine Learning Strategies
… computer vision, artificial intelligence, and machine learning. The problems with these existing solutions are that they are either complex or not reliable enough. The need for better solutions in this specific domain as well as my desire to bring my contribution to something meaningful are …
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Unveiling the impact of neuromotor disorders on speech: a structured approach combining biomechanical fundamentals and statistical machine learning
… specific mapping to articulation kinematics. The statistical methods used in performance evaluation are based on three-way comparisons and transversal and longitudinal assessment by classical hypothesis testing. Three related experimental studies are shown to empirically illustrate the potential …
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Efficient Sparse Bayesian Learning using Spike-and-Slab Priors
In the context of statistical machine learning, sparse learning is a procedure that seeks a reconciliation between two competing aspects of a statistical model: good predictive power and interpretability. In a Bayesian setting, sparse learning methods invoke sparsity inducing priors to explicitly …
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Accelerating Probabilistic Computing with a Stochastic Processing Unit
<p>Statistical machine learning becomes a more important workload for computing systems than ever before. Probabilistic computing is a popular approach in statistical machine learning, which solves problems by iteratively generating samples from parameterized distributions. As an alternative to …
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Analysis and algorithms for parametrization, optimization and customization of sled hockey equipment and other dynamical systems
… numerical algorithms, for solving ODEs/PDEs, and statistical/machine-learning algorithms based on data, for physical inference and prediction. We further apply the methodologies on sled hockey, an adaptation of stand-up hockey, allows people with physical disabilities to participate in the game of …
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Towards perceptual intelligence : statistical modeling of human individual and interactive behaviors
… correctly classify human behaviors, by means of Machine Perception and Machine Learning techniques. In the thesis I develop the statistical machine learning algorithms (dynamic graphical models) necessary for detecting and recognizing individual and interactive behaviors. In the case of the …
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Identification, improved modeling and integration of signals to predict constitutive and altering splicing
… sequence features and their integration into a statistical machine-learning algorithm, ACEScan, which distinguishes exons subject to evolutionarily conserved alternative splicing from constitutively spliced or lineage-specifically-spliced exons is described; (v) The genome-wide search for and …
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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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Examination and utilization of rare features in text classification of injury narratives
… analyzing injury surveillance data with statistical machine learning methods has grown in popularity, complexity, and quality over recent years. During that same time, researchers have recognized the limitations of statistical text analysis with limited training data. In response to the …
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Use of prior knowledge in classification of similar and structured objects
Statistical machine learning has achieved great success in many fields in the last few decades. However, there remain classification problems that computers still struggle to match human performance. Many such problems share the same properties---large within class variability and complex structure …
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Robot learning with strong priors
Embedding learning ability in robotic systems is one of the long sought-after objectives of artificial intelligence research. Despite the recent advancements in hardware, large-scale machine learning algorithms and theoretical understanding of deep learning, it is still quite unrealistic to deploy …
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Distances and Stability in Biological Network Theory
… complex networks. It will also be coupled with statistical machine learning models, in order to integrate feature selection and network inference within a pathway profiling approach. The evaluation of similarity between networks will be the first and central operative procedure of the developed …
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Efficient Multi-Target Tracking using graphical models
… algorithms that are distinguished by the use of statistical machine learning techniques. MTT is a crucial problem for many important practical applications such as military surveillance. Despite being a well-studied research problem, MTT remains challenging, mostly because of the challenges of …
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Learning matrix and functional models in high-dimensions
Statistical machine learning methods provide us with a principled framework for extracting meaningful information from noisy high-dimensional data sets. A significant feature of such procedures is that the inferences made are statistically significant, computationally efficient and scientifically …
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A Unified Robust Minimax Framework for Regularized Learning Problems
… to apply minimax related concepts to real-world learning tasks, we develop a new fault-tolerant classification framework to combat class noise for general multi-class classification problems; further, by studying the relationship between the majorizable function class and the minimax framework, …
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Engineering System Design for Automated Space Weather Forecast. Designing Automatic Software Systems for the Large-Scale Analysis of Solar Data, Knowledge Extraction and the Prediction of Solar Activities Using Machine Learning Techniques
… thesis introduces novel, fully computerised, machine learning-based decision rules and models that can be used within a system design for automated space weather forecasting. The system design in this work consists of three stages: (1) designing computer tools to find the associations among …
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Engineering System Design for Automated Space Weather Forecast. Designing Automatic Software Systems for the Large-Scale Analysis of Solar Data, Knowledge Extraction and the Prediction of Solar Activities Using Machine Learning Techniques.
… thesis introduces novel, fully computerised, machine learning-based decision rules and models that can be used within a system design for automated space weather forecasting. The system design in this work consists of three stages: (1) designing computer tools to find the associations among …
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