Download or read book Gaussian Processes for Machine Learning written by Carl Edward Rasmussen and published by MIT Press. This book was released on 2005-11-23 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.
Download or read book Kernel Functions and Elliptic Differential Equations in Mathematical Physics written by Stefan Bergman and published by Courier Corporation. This book was released on 2013-01-23 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Kernel Functions and Differential Equations written by and published by Academic Press. This book was released on 2009-08-31 with total page 447 pages. Available in PDF, EPUB and Kindle. Book excerpt: Kernel Functions and Differential Equations
Download or read book The Kernel Function and Conformal Mapping written by Stefan Bergman and published by American Mathematical Soc.. This book was released on 1950-03 with total page 269 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Kernel Methods in Computational Biology written by Bernhard Schölkopf and published by MIT Press. This book was released on 2004 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: A detailed overview of current research in kernel methods and their application to computational biology.
Download or read book Kernels for Vector Valued Functions written by Mauricio A. Álvarez and published by Foundations & Trends. This book was released on 2012 with total page 86 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Kernel Methods and Machine Learning written by S. Y. Kung and published by Cambridge University Press. This book was released on 2014-04-17 with total page 617 pages. Available in PDF, EPUB and Kindle. Book excerpt: Offering a fundamental basis in kernel-based learning theory, this book covers both statistical and algebraic principles. It provides over 30 major theorems for kernel-based supervised and unsupervised learning models. The first of the theorems establishes a condition, arguably necessary and sufficient, for the kernelization of learning models. In addition, several other theorems are devoted to proving mathematical equivalence between seemingly unrelated models. With over 25 closed-form and iterative algorithms, the book provides a step-by-step guide to algorithmic procedures and analysing which factors to consider in tackling a given problem, enabling readers to improve specifically designed learning algorithms, build models for new applications and develop efficient techniques suitable for green machine learning technologies. Numerous real-world examples and over 200 problems, several of which are Matlab-based simulation exercises, make this an essential resource for graduate students and professionals in computer science, electrical and biomedical engineering. Solutions to problems are provided online for instructors.
Download or read book Kernel Methods for Pattern Analysis written by John Shawe-Taylor and published by Cambridge University Press. This book was released on 2004-06-28 with total page 520 pages. Available in PDF, EPUB and Kindle. Book excerpt: Publisher Description
Download or read book Machine Learning for Audio Image and Video Analysis written by Francesco Camastra and published by Springer. This book was released on 2015-07-21 with total page 564 pages. Available in PDF, EPUB and Kindle. Book excerpt: This second edition focuses on audio, image and video data, the three main types of input that machines deal with when interacting with the real world. A set of appendices provides the reader with self-contained introductions to the mathematical background necessary to read the book. Divided into three main parts, From Perception to Computation introduces methodologies aimed at representing the data in forms suitable for computer processing, especially when it comes to audio and images. Whilst the second part, Machine Learning includes an extensive overview of statistical techniques aimed at addressing three main problems, namely classification (automatically assigning a data sample to one of the classes belonging to a predefined set), clustering (automatically grouping data samples according to the similarity of their properties) and sequence analysis (automatically mapping a sequence of observations into a sequence of human-understandable symbols). The third part Applications shows how the abstract problems defined in the second part underlie technologies capable to perform complex tasks such as the recognition of hand gestures or the transcription of handwritten data. Machine Learning for Audio, Image and Video Analysis is suitable for students to acquire a solid background in machine learning as well as for practitioners to deepen their knowledge of the state-of-the-art. All application chapters are based on publicly available data and free software packages, thus allowing readers to replicate the experiments.
Download or read book Kernel Functions Analytic Torsion and Moduli Spaces written by John David Fay and published by American Mathematical Soc.. This book was released on 1992 with total page 137 pages. Available in PDF, EPUB and Kindle. Book excerpt: This memoir is a study of Ray-Singer analytic torsion for hermitian vector bundles on a compact Riemann surface [italic]C. The torsion is expressed through the trace of a modified resolvent. Thus, one can develop perturbation-curvature formulae for the Green-Szegö kernel and also for the torsion in terms of the Ahlfors-Bers complex structure of the Teichmuller space and Mumford complex structure of the moduli space of stable bundles of degree zero on [italic]C.
Download or read book Kernel Methods in Computer Vision written by Christoph H. Lampert and published by Now Publishers Inc. This book was released on 2009 with total page 113 pages. Available in PDF, EPUB and Kindle. Book excerpt: Few developments have influenced the field of computer vision in the last decade more than the introduction of statistical machine learning techniques. Particularly kernel-based classifiers, such as the support vector machine, have become indispensable tools, providing a unified framework for solving a wide range of image-related prediction tasks, including face recognition, object detection and action classification. By emphasizing the geometric intuition that all kernel methods rely on, Kernel Methods in Computer Vision provides an introduction to kernel-based machine learning techniques accessible to a wide audience including students, researchers and practitioners alike, without sacrificing mathematical correctness. It covers not only support vector machines but also less known techniques for kernel-based regression, outlier detection, clustering and dimensionality reduction. Additionally, it offers an outlook on recent developments in kernel methods that have not yet made it into the regular textbooks: structured prediction, dependency estimation and learning of the kernel function. Each topic is illustrated with examples of successful application in the computer vision literature, making Kernel Methods in Computer Vision a useful guide not only for those wanting to understand the working principles of kernel methods, but also for anyone wanting to apply them to real-life problems.
Download or read book Theta Functions Kernel Functions and Abelian Integrals written by Dennis A. Hejhal and published by American Mathematical Soc.. This book was released on 1972 with total page 119 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Learning with Kernels written by Bernhard Scholkopf and published by MIT Press. This book was released on 2018-06-05 with total page 645 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive introduction to Support Vector Machines and related kernel methods. In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs—-kernels—for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics. Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.
Download or read book Transformations Transmutations and Kernel Functions Volume II written by H Begehr and published by CRC Press. This book was released on 2023-06-16 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Bridging the Gap Between Graph Edit Distance and Kernel Machines written by Michel Neuhaus and published by World Scientific. This book was released on 2007 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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, thus combining the flexibility of edit distance-based graph matching with the power of kernel machines for pattern recognition. The authors introduce a collection of novel graph kernels related to edit distance, including diffusion kernels, convolution kernels, and random walk kernels. From an experimental evaluation of a semi-artificial line drawing data set and four real-world data sets consisting of pictures, microscopic images, fingerprints, and molecules, the authors demonstrate that some of the kernel functions in conjunction with support vector machines significantly outperform traditional edit distance-based nearest-neighbor classifiers, both in terms of classification accuracy and running time."
Download or read book Schur Functions Operator Colligations and Reproducing Kernel Pontryagin Spaces written by Daniel Alpay and published by Birkhäuser. This book was released on 2012-12-06 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generalized Schur functions are scalar- or operator-valued holomorphic functions such that certain associated kernels have a finite number of negative squares. This book develops the realization theory of such functions as characteristic functions of coisometric, isometric, and unitary colligations whose state spaces are reproducing kernel Pontryagin spaces. This provides a modern system theory setting for the relationship between invariant subspaces and factorization, operator models, Krein-Langer factorizations, and other topics. The book is intended for students and researchers in mathematics and engineering. An introductory chapter supplies background material, including reproducing kernel Pontryagin spaces, complementary spaces in the sense of de Branges, and a key result on defining operators as closures of linear relations. The presentation is self-contained and streamlined so that the indefinite case is handled completely parallel to the definite case.
Download or read book Digital Signal Processing with Kernel Methods written by Jose Luis Rojo-Alvarez and published by John Wiley & Sons. This book was released on 2018-02-05 with total page 665 pages. Available in PDF, EPUB and Kindle. Book excerpt: A realistic and comprehensive review of joint approaches to machine learning and signal processing algorithms, with application to communications, multimedia, and biomedical engineering systems Digital Signal Processing with Kernel Methods reviews the milestones in the mixing of classical digital signal processing models and advanced kernel machines statistical learning tools. It explains the fundamental concepts from both fields of machine learning and signal processing so that readers can quickly get up to speed in order to begin developing the concepts and application software in their own research. Digital Signal Processing with Kernel Methods provides a comprehensive overview of kernel methods in signal processing, without restriction to any application field. It also offers example applications and detailed benchmarking experiments with real and synthetic datasets throughout. Readers can find further worked examples with Matlab source code on a website developed by the authors: http://github.com/DSPKM • Presents the necessary basic ideas from both digital signal processing and machine learning concepts • Reviews the state-of-the-art in SVM algorithms for classification and detection problems in the context of signal processing • Surveys advances in kernel signal processing beyond SVM algorithms to present other highly relevant kernel methods for digital signal processing An excellent book for signal processing researchers and practitioners, Digital Signal Processing with Kernel Methods will also appeal to those involved in machine learning and pattern recognition.
