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 14 of 14 for “"Kernel Machines"”.
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Kernel Machines are Not Black Boxes - On the Interpretability of Kernel-based Nonparametric Models
Kernel-based nonparametric models are often perceived as uninterpretable black boxes. This notion is however questionable; permutation-based variable importance, for example, is one approach for interpreting various nonparametric models. Unfortunately, besides being computationally intensive, …
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Shallow and deep learning for audio and natural language processing
… of training data. Deep learning models and kernel machines are regarded as models with high expressive power through the composition of multiple layers of nonlinearities and through nonlinearly mapping data to a high-dimensional space, respectively. While the majority of deep learning work …
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Local Approaches for Fast, Scalable and Accurate Learning with Kernels
… SVM approach, (ii) a family of operators on kernels to obtain Quasi-Local kernels, (iii) the framework of Local Kernel Machines, and (iv) a local maximal margin approach to noise reduction. In our analysis of Local SVM, we derive a new learning bound starting from the theory of Local Learning …
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Foundations of Machine Learning: Over-parameterization and Feature Learning
… simplifies to training classical models known as kernel machines using the Neural Tangent Kernel (NTK). We showcase the practical value of the NTK by deriving and using it for matrix completion problems such as image inpainting and virtual drug screening. Additionally, we use the NTK connection to …
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Training hierarchical networks for function approximation
… Networks (DCN) can be equivalent to hierarchical kernel machines with the Radial Basis Functions (RBF).We investigate the difficulty of training RBF networks with stochastic gradient descent (SGD) and hierarchical RBF. We discovered that training singled layered RBF networks can be quite simple …
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Semi-Parametric Testing of Single-Nucleotide Polymorphism Effects On Continuous Outcome
… we explored a semi-parametric method built upon kernel machines to include multiple SNPs in a model. The information of SNPs similarity calculated based on identity-by-state (IBS) algorithm was included in a kernel matrix. We evaluated the method via various scenarios based on simulation studies. …
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Machine Learning Methods for Learning Genetic Dependencies
… a recently developed class of feature learning kernel machines known as Recursive Feature Machines (RFMs) to develop a pipeline for identifying SL pairs based on CRISPR viability data from DepMap. In particular, we first train RFMs to predict viability scores for a given CRISPRgene knockout from …
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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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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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Structure in Machine Learning: Graphical Models and Monte Carlo Methods
… reduction and approximate inference in kernel methods. Approximate inference is a fundamental problem in machine learning and statistics, with strong connections to other domains such as theoretical computer science. At the same time, there has often been a gap between the success of …
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Efficient learning machines
… This thesis is on adapting learning-based machines to these emerging prospects. In particular, subject of this work are three distinct research topics with the underlying drivers: the wish to reliably predict (a) given a large number of classes, (b) given a large number of samples, and (c) …
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Large-Scale Machine Learning for Classification and Search
… key methods to build scalable semi-supervised kernel machines such that real-world linearly inseparable data can be tackled. The proposed techniques take advantage of the Anchor Graph from a kernel point of view, generating a set of low-rank kernels which are made to encompass the neighborhood …
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Classification with Large Sparse Datasets: Convergence Analysis and Scalable Algorithms
… Meanwhile, most non-linear methods such as the kernel machines cannot scale to very large and high-dimensional datasets. The third part of this thesis studies how to efficiently learn non-linear structures in large sparse data. Towards this goal, we develop novel scalable feature mappings that …
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An Explainable Artificial Intelligence Approach Based on Deep Type-2 Fuzzy Logic System
… such as Deep Neural Networks, support vector machines, etc., could lead to a lack of transparency. This lack of transparency is not specific to deep learning or complex AI algorithms; other interpretable AI algorithms such as kernel machines, logistic regressions, decision trees, or …