An Introduction To Mathematical Learning Theory

DOWNLOAD

Download An Introduction To Mathematical Learning Theory PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get An Introduction To Mathematical Learning Theory book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page

An Introduction To Mathematical Learning Theory

DOWNLOAD
Author : Richard C. Atkinson
language : en
Publisher:
Release Date : 1965

An Introduction To Mathematical Learning Theory written by Richard C. Atkinson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1965 with Learning, Psychology of categories.

An Introduction To Mathematical Learning Theory

DOWNLOAD
Author : Richard Chatham ATKINSON
language : en
Publisher:
Release Date : 1965

An Introduction To Mathematical Learning Theory written by Richard Chatham ATKINSON and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1965 with Learning, Psychology of categories.

An Introduction To Mathematical Learning Theory

DOWNLOAD
Author : Richard C. Atkinson
language : en
Publisher:
Release Date : 1965

An Introduction To Mathematical Learning Theory written by Richard C. Atkinson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1965 with Learning, Psychology of categories.

An Introduction To Mathematical Learning Theory

DOWNLOAD
Author : Richard C. Atkinson
language : en
Publisher:
Release Date : 1965

An Introduction To Mathematical Learning Theory written by Richard C. Atkinson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1965 with Learning, Psychology of categories.

Mathematical Theories Of Machine Learning Theory And Applications

DOWNLOAD
Author : Bin Shi
language : en
Publisher: Springer
Release Date : 2019-06-12

Mathematical Theories Of Machine Learning Theory And Applications written by Bin Shi and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-12 with Technology & Engineering categories.

This book studies mathematical theories of machine learning. The first part of the book explores the optimality and adaptivity of choosing step sizes of gradient descent for escaping strict saddle points in non-convex optimization problems. In the second part, the authors propose algorithms to find local minima in nonconvex optimization and to obtain global minima in some degree from the Newton Second Law without friction. In the third part, the authors study the problem of subspace clustering with noisy and missing data, which is a problem well-motivated by practical applications data subject to stochastic Gaussian noise and/or incomplete data with uniformly missing entries. In the last part, the authors introduce an novel VAR model with Elastic-Net regularization and its equivalent Bayesian model allowing for both a stable sparsity and a group selection.

An Introduction To The Mathematical Theory Of Inverse Problems

DOWNLOAD
Author : Andreas Kirsch
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-03-24

An Introduction To The Mathematical Theory Of Inverse Problems written by Andreas Kirsch and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-03-24 with Mathematics categories.

This book introduces the reader to the area of inverse problems. The study of inverse problems is of vital interest to many areas of science and technology such as geophysical exploration, system identification, nondestructive testing and ultrasonic tomography. The aim of this book is twofold: in the first part, the reader is exposed to the basic notions and difficulties encountered with ill-posed problems. Basic properties of regularization methods for linear ill-posed problems are studied by means of several simple analytical and numerical examples. The second part of the book presents two special nonlinear inverse problems in detail - the inverse spectral problem and the inverse scattering problem. The corresponding direct problems are studied with respect to existence, uniqueness and continuous dependence on parameters. Then some theoretical results as well as numerical procedures for the inverse problems are discussed. The choice of material and its presentation in the book are new, thus making it particularly suitable for graduate students. Basic knowledge of real analysis is assumed. In this new edition, the Factorization Method is included as one of the prominent members in this monograph. Since the Factorization Method is particularly simple for the problem of EIT and this field has attracted a lot of attention during the past decade a chapter on EIT has been added in this monograph as Chapter 5 while the chapter on inverse scattering theory is now Chapter 6.The main changes of this second edition compared to the first edition concern only Chapters 5 and 6 and the Appendix A. Chapter 5 introduces the reader to the inverse problem of electrical impedance tomography.

Mathematics For Machine Learning

DOWNLOAD
Author : Marc Peter Deisenroth
language : en
Publisher: Cambridge University Press
Release Date : 2020-04-23

Mathematics For Machine Learning written by Marc Peter Deisenroth and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-23 with Computers categories.

Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning.

Learning Theory

DOWNLOAD
Author : Felipe Cucker
language : en
Publisher: Cambridge University Press
Release Date : 2007-03-29

Learning Theory written by Felipe Cucker and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-03-29 with Computers categories.

The goal of learning theory is to approximate a function from sample values. To attain this goal learning theory draws on a variety of diverse subjects, specifically statistics, approximation theory, and algorithmics. Ideas from all these areas blended to form a subject whose many successful applications have triggered a rapid growth during the last two decades. This is the first book to give a general overview of the theoretical foundations of the subject emphasizing the approximation theory, while still giving a balanced overview. It is based on courses taught by the authors, and is reasonably self-contained so will appeal to a broad spectrum of researchers in learning theory and adjacent fields. It will also serve as an introduction for graduate students and others entering the field, who wish to see how the problems raised in learning theory relate to other disciplines.

Problems In Mathematical Learning Theory With Solutions

DOWNLOAD
Author : Richard C. Atkinson
language : en
Publisher:
Release Date : 1966

Problems In Mathematical Learning Theory With Solutions written by Richard C. Atkinson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1966 with categories.

Machine Learning

DOWNLOAD
Author : RODRIGO F MELLO
language : en
Publisher: Springer
Release Date : 2018-08-01

Machine Learning written by RODRIGO F MELLO and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-01 with Computers categories.

This book presents the Statistical Learning Theory in a detailed and easy to understand way, by using practical examples, algorithms and source codes. It can be used as a textbook in graduation or undergraduation courses, for self-learners, or as reference with respect to the main theoretical concepts of Machine Learning. Fundamental concepts of Linear Algebra and Optimization applied to Machine Learning are provided, as well as source codes in R, making the book as self-contained as possible. It starts with an introduction to Machine Learning concepts and algorithms such as the Perceptron, Multilayer Perceptron and the Distance-Weighted Nearest Neighbors with examples, in order to provide the necessary foundation so the reader is able to understand the Bias-Variance Dilemma, which is the central point of the Statistical Learning Theory. Afterwards, we introduce all assumptions and formalize the Statistical Learning Theory, allowing the practical study of different classification algorithms. Then, we proceed with concentration inequalities until arriving to the Generalization and the Large-Margin bounds, providing the main motivations for the Support Vector Machines. From that, we introduce all necessary optimization concepts related to the implementation of Support Vector Machines. To provide a next stage of development, the book finishes with a discussion on SVM kernels as a way and motivation to study data spaces and improve classification results.