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 12 of 12 for “"Kernel Ridge Regression"”.
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Essays on Algorithmic Learning and Uncertainty Quantification
… convergence rates for non-parametric logistic regression in non-convex models. The second essay, titled “Kernel Ridge Regression Inference,” introduces a new technique for deriving sharp, non-asymptotic, uniform Gaussian approximation for partial sums in a reproducing kernel Hilbert space, …
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Diversity-inducing probability measures for machine learning
… problems arise in machine learning within kernel approximation, experimental design, and numerous other applications. In such applications, one often seeks to select diverse subsets of items to represent the population. One way to select such diverse subsets is to sample according to …
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Mathematical analysis of uncertainty in machine learning and deep learning
… a confidence band to assess uncertainty of a kernel ridge regression estimator. We propose the formulation to obtain a confidence band as the convex optimization, which enables us to use existing algorithms such as the primal-dual inner point method. The proposed method acquires a more …
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Sequential decision making with feature-linear models
… where the rewards are linear in a reproducing kernel Hilbert space, and a reinforcement learning setting with features given by a neural network. The thesis is split into two parts accordingly. In part I, we introduce a new algorithm for the optimisation of continuous functions with a known …
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New techniques in low-Q² elastic electron-proton scattering measurements and the proton radius extraction
… non-parametric models. We demonstrate that the kernel ridge regression and the Gaussian process have similar levels of performance compared to the traditional function fitting approaches. Our extracted values from different data sets still show the discrepancy of the proton charge radius, …
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Efficient sampling for determinantal point processes
… chain (k-)DPP under the condition that data kernel matrix is sparse. Concretely, we present a general framework for accelerating algorithms that requires computation of uT A-1u as one of computational subroutines. In our framework, we bound uT A-1u with Gauss-type quadrature efficiently. We …
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DISCOVERY OF HIGH ENTROPY CERAMICS WITH LOW THERMAL CONDUCTIVITY THROUGH MACHINE LEARNING
… In the next learning process, various nonlinear regression ML models, including Kernel Ridge Regression (KRR), Random Forest Regression (RF), Support Vector Regression (SVR), and eXtreme Gradient Boosting (XGBoost) are employed. As a result, the RF, KRR, and XGBoost exhibit excellent performance, …
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Machine Learning Estimation of Reaction Energy Barriers and its Applications in Astrochemistry
… all be obtained at a small computational cost. A Kernel Ridge Regression with Laplacian kernel was found to give the best fit to the data. It makes predictions with a mean absolute error of 4.1 kcal/mol for barriers smaller than 40 kcal/mol. We used this machine learning model to estimate the …
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Optimizing deep learning networks using multi-armed bandits
… trees, SVM, Naïve Bayes, LDA, QDA, logistic regression, Gaussian process classifier, kernel ridge regression, LASSO regression, linear regression, Bayesian Ridge regression, boosting, bagging and random forests. The results on the data sets show that some of the new methods (i) generalize …
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Scalable Approximate Inference and Model Selection in Gaussian Process Regression
… datasets due to the need to compute a large kernel matrix and perform standard linear-algebraic operations with this matrix. This limitation has driven decades of research in both statistics and machine learning seeking to scale Gaussian process regression models to ever-larger datasets. This …
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An investigation into the dynamic implementation of a 16-electrode FDM Electrical Impedance Tomography System
… method has been proposed and is investigated. Kernel Ridge Regression (KRR) is an 'intelligent' generalisation technique which has been demonstrated to outperform classical NN type approaches in similar problems. The experiments of this research benchmark its performance against the best …
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Machine Learning based Surrogate Modeling of Electronic Devices and Circuits
L'abstract è presente nell'allegato / the abstract is in the attachment