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 46 for “"Kernel methods"”.
-
Localized Kernel Methods for Signal Processing
… dissertation presents two signal processing methods using specially designed localized kernels for parameter recovery under noisy condition. The first method addresses the estimation of frequencies and amplitudes in multidimensional exponential models. It utilizes localized trigonometric …
-
Kernel Methods for Tree Structured Data
… in structured form. Since traditional methods in machine learning deal with vectorial information, they require an a priori form of preprocessing. Among all the learning techniques for dealing with structured data, kernel methods are recognized to have a strong theoretical background …
-
Nonlinear dynamic process monitoring using kernel methods
The application of kernel methods in process monitoring is well established. How- ever, there is need to extend existing techniques using novel implementation strate- gies in order to improve process monitoring performance. For example, process monitoring using kernel principal component analysis …
-
Convergence of Kernel Methods for Modeling and Estimation of Dynamical Systems
… also arise regarding the reliability of these methods. Recent developments in approximation theory provide a new perspective to studying these problems. This dissertation analyzes the convergence of two kernel-based, data-driven modeling methods, the reproducing kernel Hilbert space (RKHS) …
-
Transformation knowledge in pattern analysis with kernel methods - distance and integration kernels
… data analysis and machine learning are so called kernel methods. The most famous and successful one is represented by the support vector machine (SVM) for classification or regression tasks. Further examples are kernel principal component analysis for feature extraction or other linear classifiers …
-
Group transformation and identification with kernel methods and big data mixed logistic regression
… of data analysis. Exploring data with effective methods would reveal main characteristics of data and provides guidance for model building. The goal of this thesis is to develop effective and efficient methods for data exploration in the regression setting.</p> <p>First, we propose to use optimal …
-
Direct and inverse problems in machine learning
… Our estimators are all constructed by kernel methods, which depend on a Reproducing Kernel Hilbert Space structure using spectral regularization methods. A first main result establishes upper and lower bounds for the rate of convergence under a given source condition assumption, …
-
Deep Embedding Kernel
Kernel methods and deep learning are two major branches of machine learning that have achieved numerous successes in both analytics and artificial intelligence. While having their own unique characteristics, both branches work through mapping data to a feature space that is supposedly more …
-
Faster linear algebra for data analysis and machine learning
… regression, data clustering, and nonlinear kernel methods. To scale these problems to massive datasets, we design new algorithms based on random sampling and iterative refinement, tools that have become an essential part of modern computational linear algebra. We focus on methods that are …
-
Gradient-Based Sensitivity Analysis with Kernels
… for the active subspace of a function using kernel methods to indirectly estimate the gradient. We then demonstrate how to deploy the learned input directions to improve the predictive performance of local regression models by ``undoing" the active subspace. Finally, we develop notions of …
-
Enhanced Weak Signal Detection Using SVM Based Correlation Algorithm
… SNR data. However, the assumptions behind these methods begin to fall apart when signal period becomes either very short, or small in amplitude compared to any corruptive noise. To address this a kernel transform based cross-correlation algorithm is proposed for the application of weak signal …
-
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, …
-
Estimating time delays between irregularly sampled time series
… Estimations have been made using statistical methods in the astrophysics literature, such as interpolation, dispersion analysis, discrete correlation function, Gaussian processes and Bayesian method, among others. Instead, this thesis proposes a kernel-based approach to estimating the time …
-
Approximating a wavelet kernel using a quantum computer
… can aid or improve classical machine learning methods, or whether quantum computers can perform machine learning tasks which classical computers cannot. In this thesis we explore different implementations of quantum machine learning algorithms on near term quantum computers, and the limits of …
-
Probabilistic data analysis with probabilistic programming
… hierarchical Bayesian models, multivariate kernel methods, discriminative machine learning, clustering algorithms, dimensionality reduction, and arbitrary probabilistic programs. We also demonstrate the integration of CGPMs into BayesDB, a probabilistic programming platform that can express …
-
Problems related to efficacy measurement and analyses
… objectively compared to the earlier approaches. Kernel density estimation is a useful non-parametric method that has not been well utilized as an applied statistical tool, mainly due to its computational complexity. The current study shows that this method is robust even under correlation …
-
Efficient learning machines
… Other cornerstones of Machine Learning are kernel methods and neural networks. The ever-growing data collections expose a scaling issue of kernel methods with respect to a large number of data points. The predominant approach to alleviate this is an approximation based on random features. In …
-
Efficient and Robust Black-box Integral-approximation and Optimization
… algorithm from the perspective of frequentist kernel methods, which is powerful for low-dimensional black-box optimization problems. We further apply our structured integral approximation techniques for kernel approximation. In addition, we develop structured approximation for robust deep …
-
Machine Learning for Information Extraction
… as a classification problem, and applies kernel methods to learn the relation classifiers. Novel kernels are defined in terms of shallow parses, and efficient algorithms are given for computing the kernels. The study evaluates the kernel approach experimentally, with positive results. The …
-
Quantum Machine Learning Applied to Astronomical Datasets
… clustering, and anomaly detection. Quantum kernel methods, such as quantum-enhanced support vector machines and quantum-enhanced spectral clustering, as well as quantum variational circuits, including quantum convolutional neural networks and quantum autoencoders, are evaluated in comparison …
Page 1 of 3