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 1439 for “"Kernel"”.
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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 …
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Sparse Kernel feature extraction
… The analysis results in a new derivation for Kernel Feature Analysis (KFA) and the formation of two novel matrix approximation methods based on PLS. In the supervised case, we apply the general feature extraction framework to derive two new methods based on maximising covariance and alignment …
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Probabilistic multiple kernel learning
The integration of multiple and possibly heterogeneous information sources for an overall decision-making process has been an open and unresolved research direction in computing science since its very beginning. This thesis attempts to address parts of that direction by proposing probabilistic data …
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Kernel-based association measures
… existing methods and novel extensions based on kernels, including practical solutions to computational challenges. The proposed framework provides improved feature selection and extensions to a variety of current classifiers. Specifically, we introduce association screening and variable …
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High Performance Inter-kernel Communication and Networking in a Replicated-kernel Operating System
… operating system architectures is the Multi-kernel. Multi-kernels not only address scalability issues, but also inherently support heterogeneity. Furthermore, provide an easy way to properly map computing workloads to the correct type of processing resources in presence of heterogeneity. …
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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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lp-Norm Multiple Kernel Learning
… zu erhalten, entwickle ich die lp-norm multiple kernel learning Methodologie, die sich effizienter und effektiver als vorherige Lösungsansätze erweist. Insbesondere leite ich Algorithmen zur Optimierung des Problems her, die wesentlich schneller sind als existierende und es erlauben, gleichzeitig …
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OS INSPIRED COMPLETE KERNEL FUSION
… communication is integrated directly into GPU kernels. We realize this vision in FlashDMoE: a persistent, in-kernel, actor-style operating system with packet switching that enables complete operator fusion for Distributed MoE (DMoE) into a single kernel, the first of its kind. FlashDMoE …
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A microprogrammed operating system kernel
… design and implementation of an operating system kernel for the Cambridge Capability Computer (CAP). The kernel of an operating syst em is its most primitive level of facilities and forms the foundation stone a round which t he rest of the system is structured. The particular emphasis of the CAP …
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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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Kernel Estimators in Complex Data Analysis
Kernel estimators, including kernel density estimators and kernel regression estimators, have drawn great research interests in terms of both theoretical studies and applications since invention, due to their easy interpretation and flexibility to model data with complicated density …
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Renormierungsgruppen-Flussgleichungen im Heat-Kernel-Formalismus
… Dazu benutzen wir die Methode der Heat-Kernel-RG-Flußgleichungen. Die RG-Skala wird über einen sogenannten Heat-Kernel-cutoff eingeführt, der die effektive Wirkung in der Schwinger-Eigenzeit-Darstellung regularisiert. Der Vorteil dieser Methode besteht darin, daß der Heat-Kernel-cutoff …
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Heat kernel estimates on glued spaces
In this thesis, we prove heat kernel estimates in two main contexts: (1) manifolds with ends with mixed Dirichlet and Neumann boundary condition and (2) infinite (countable) graphs satisfying certain properties, which we call book-like graphs. In both of these settings, we start with ''sufficiently …
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Localized Kernel Methods for 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 polynomial kernels to detect the multivariate …
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Kernel Methods for Tree Structured Data
… techniques for dealing with structured data, kernel methods are recognized to have a strong theoretical background and to be effective approaches. They do not require an explicit vectorial representation of the data in terms of features, but rely on a measure of similarity between any pair of …
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The kernel of ad hoc polymorphism
Ad hoc polymorphism allows a value to take on multiple types, with a separate definition of the value provided for each type. We offer a new formalization of this old concept as a typed lambda calculus. Motivated by the aspiration of extending System F with ad hoc constraints, we introduce a new …
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Bias Assessment and Reduction in Kernel Smoothing
… local polynomial regression (LPR) with kernel smoothing, the choice of the smoothing parameter, or bandwidth, is critical. The performance of the method is often evaluated using the Mean Square Error (MSE). Bias and variance are two components of MSE. Kernel methods are known to exhibit …
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