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Kernel Functions and Differential Equations
Kernel Functions and Differential Equations
Gaussian Processes for Machine Learning
A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both ...
Kernel Functions and Elliptic Differential Equations in Mathematical Physics
Covers the theory of boundary value problems in partial differential equations and discusses a portion of the theory from a unifying point of view while providing an introduction to each branch of its applications. 1953 edition.
Theta Functions, Kernel Functions and Abelian Integrals
This monograph presents many interesting results, old and new, about theta functions, Abelian integrals and kernel functions on closed Riemann surfaces. It begins with a review of classical kernel function theory for plane domains. Next there is a discussion of function theory on closed Riemann surfaces, leading to explicit formulas for Szegö kernels in terms of the Klein prime function and theta functions. Later sections develop explicit relations between the classical Szegö and Bergman kernels and between the Szegö and modified (semi-exact) Bergman kernels. The author's results allow him to solve an open problem mentioned by L. Sario and K. Oikawa in 1969.
Kernel Functions and Elliptic Differential Equations in Mathematical Physics
This text focuses on the theory of boundary value problems in partial differential equations, which plays a central role in various fields of pure and applied mathematics, theoretical physics, and engineering. Geared toward upper-level undergraduates and graduate students, it discusses a portion of the theory from a unifying point of view and provides a systematic and self-contained introduction to each branch of the applications it employs.
The Kernel Function and Conformal Mapping
The Kernel Function and Conformal Mapping by Stefan Bergman is a revised edition of ""The Kernel Function"". The author has made extensive changes in the original volume. The present book will be of interest not only to mathematicians, but also to engineers, physicists, and computer scientists. The applications of orthogonal functions in solving boundary value problems and conformal mappings onto canonical domains are discussed; and publications are indicated where programs for carrying out numerical work using high-speed computers can be found.The unification of methods in the theory of functions of one and several complex variables is one of the purposes of introducing the kernel function and the domains with a distinguished boundary. This approach has been extensively developed during the last two decades. This second edition of Professor Bergman's book reviews this branch of the theory including recent developments not dealt with in the first edition. The presentation of the topics is simple and presupposes only knowledge of an elementary course in the theory of analytic functions of one variable.
Kernels for Vector-Valued Functions
This monograph reviews different methods to design or learn valid kernel functions for multiple outputs, paying particular attention to the connection between probabilistic and regularization methods.
Kernel Methods in Computational Biology
A detailed overview of current research in kernel methods and their application to computational biology.
Bridging the Gap Between Graph Edit Distance and Kernel Machines
In graph-based structural pattern recognition, the idea is to transform patterns into graphs and perform the analysis and recognition of patterns in the graph domain OCo commonly referred to as graph matching. A large number of methods for graph matching have been proposed. Graph edit distance, for instance, defines the dissimilarity of two graphs by the amount of distortion that is needed to transform one graph into the other and is considered one of the most flexible methods for error-tolerant graph matching.This book focuses on graph kernel functions that are highly tolerant towards structural errors. The basic idea is to incorporate concepts from graph edit distance into kernel functions...
Transformations, Transmutations, and Kernel Functions, Volume II
Complex analytical methods are a powerful tool for special partial differential equations and systems. To make these methods applicable for a wider class, transformations and transmutations are used.
