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 18 of 18 for “"Kernel Machine"”.
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Learning with kernel machine architectures
Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.
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Generalization of kernel machine methods for association testing of multi-omics data
… We perform these analyses using the kernel machine regression (KMR) testing framework. Within this context, we propose three projects. For project one, we extend an existing KMR testing method to accommodate joint association testing of two data types with a trait of interest in …
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Bayesian Variable Selection and Inference for Nonparametric Kernel Machine and Functional Models
… is developed under a generalized fused multi-kernel machine regression. This method can apply to continuous/binary/ordered categorical response variables. We demonstrate the advantage of our method using bio-photonics Raman spectroscopy to identify which molecular fingerprinting wavenumber is …
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Bayesian Inference Based on Nonparametric Regression for Highly Correlated and High Dimensional Data
… and high dimensional data. Firstly, group multi-kernel machine regression (GMM) is proposed to identify the association between two sets of multidimensional functions, offering flexibility to effectively capture the complex association among high-dimensional variables. Secondly, semiparametric …
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Some Advanced Model Selection Topics for Nonparametric/Semiparametric Models with High-Dimensional Data
… topic, we propose a Nonnegative Garrote on a Kernel machine (NGK) to recover sparsity of input variables in smoothing functions. We model the smoothing function by a least squares kernel machine and construct a nonnegative garrote on the kernel model as the function of the similarity matrix. …
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Semiparametric and Nonparametric Methods for Complex Data
… second topic, we propose a joint semiparametric kernel machine network approach to provide a connection between variable selection and network estimation. Our approach is a unified and integrated method that can simultaneously identify important variables and build a network among them. We …
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Below P vs NP : fine-grained hardness for big data problems
… hardness results for several text analysis and machine learning tasks: ** Lower bounds for edit distance, regular expression matching and other pattern matching and string processing problems. ** Lower bounds for empirical risk minimization such as kernel support vectors machines and other …
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Datasets, features, learning, and models in visual recognition
… when training sophisticated classifiers such as kernelized SVM. This dissertation proposes a fast training algorithm called Stochastic Intersection Kernel Machine (SIKMA). This proposed training method will be useful for many vision problems, as it can produce a kernel classifier that is more …
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Health disparities, environmental toxicants, and midlife women’s health outcomes
… weighted quantile sum regression, and Bayesian kernel machine regression for both continuous and binomial outcome variables. Last, mediation analysis was conducted in the MWHS to understand racial and socioeconomic differences in the associations of phthalate exposure and hot flashes. This work …
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High-dimensional Multimodal Bayesian Learning
… we propose a multi-level nonparametric kernel machine approach, utilizing variational inference to jointly identify multi-level variables as well as build the network. Chapter 3 addresses the development of a simultaneous selection of functional domain subsets, selection of functional …
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Bayesian Multilevel-multiclass Graphical Model
… select Gaussian process in semiparametric multi-kernel machine regression. The first problem is approached by Gaussian graphical model. In this project, I consider learning multiple connected graphs among multilevel variables from unknown classes. I esti- mate the classes of the observations from …
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Local Approaches for Fast, Scalable and Accurate Learning with Kernels
The present thesis deals with the fundamental machine learning issues of increasing the accuracy of learning systems and their computational performances. The key concept which is exploited throughout the thesis, is the tunable trade-off between local and global approaches to learning, integrating …
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Statistical Methods for Genetic Pathway-Based Data Analysis
… pathway effect is estimated via the kernel machine and the unknown link function is estimated by transforming a mixture of beta cumulative density functions. Our approach provides flexible semiparametric settings to describe the complicated association between gene microarray …
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Building a Cognitive Radio: From Architecture Definition to Prototype Implementation
… system called the cognitive engine, and the kernel machine learning mechanism called the cognition cycle. Next, this dissertation discusses the design of specific functional building blocks which incorporate environment awareness, solution making, and adaptation. These building blocks are …
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Early life exposure to metals and adolescent neurodevelopment
… regression, quantile g-computation and Bayesian kernel machine regression. In the second and third aims, we also stratified analysis by sex to examine effect measure modification. Results: In the first aim, we found that temporal trends of tooth metal levels differed by metals and correlates of …
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Semiparametric Bayesian Kernel Survival Model for Highly Correlated High-Dimensional Data
… populations using Bayesian survival kernel models. By connecting kernel machines with semiparametric Bayesian hierarchical models, the proposed unified model frameworks can identify significant elements as well as sets regardless of mis-specifications of distributions or kernels. The …
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Data-driven modeling and transportation data analytics
… data-driven algorithm was developed based on the kernel machine to extract driving patterns from trajectory data, which avoids subjective biases in traditional physical models. A particular focus has been paid to analyzing the asymmetry phenomena in driving behavior. The study successfully proved …
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Large-Scale Machine Learning for Classification and Search
… or billions, can be collected for training machine learning models. Inspired by this trend, this thesis is dedicated to developing large-scale machine learning techniques for the purpose of making classification and nearest neighbor search practical on gigantic databases. Our first approach …