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Cover: 9780521865593 | Learning Theory | Felipe Cucker (u. a.) | Buch | Englisch | 2015
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Learning Theory
Buch von Felipe Cucker (u. a.)
Sprache: Englisch

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Beschreibung
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.
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.
Über den Autor
Felipe Cucker is a Professor of Mathematics at the City University of Hong Kong.
Zusammenfassung
The goal of learning theory is to approximate a function from sample values. To attain this goal it draws on statistics, approximation theory and algorithmics. This book aims to give a general overview of the theoretical foundations of learning theory, and it is the first to emphasize the approximation theory viewpoint. This emphasis fulfils two purposes: it provides a balanced viewpoint of the subject, and will attract mathematicians working on related fields who will find the problems raised in learning theory close to their interests.
Inhaltsverzeichnis
Preface; Foreword; 1. The framework of learning; 2. Basic hypothesis spaces; 3. Estimating the sample error; 4. Polynomial decay approximation error; 5. Estimating covering numbers; 6. Logarithmic decay approximation error; 7. On the bias-variance problem; 8. Regularization; 9. Support vector machines for classification; 10. General regularized classifiers; Bibliography; Index.
Details
Erscheinungsjahr: 2015
Genre: Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Buch
ISBN-13: 9780521865593
ISBN-10: 052186559X
Sprache: Englisch
Ausstattung / Beilage: HC gerader Rücken kaschiert
Einband: Gebunden
Autor: Cucker, Felipe
Zhou, Ding Xuan
Hersteller: Cambridge University Press
Maße: 235 x 157 x 19 mm
Von/Mit: Felipe Cucker (u. a.)
Erscheinungsdatum: 10.02.2015
Gewicht: 0,545 kg
Artikel-ID: 102080228
Über den Autor
Felipe Cucker is a Professor of Mathematics at the City University of Hong Kong.
Zusammenfassung
The goal of learning theory is to approximate a function from sample values. To attain this goal it draws on statistics, approximation theory and algorithmics. This book aims to give a general overview of the theoretical foundations of learning theory, and it is the first to emphasize the approximation theory viewpoint. This emphasis fulfils two purposes: it provides a balanced viewpoint of the subject, and will attract mathematicians working on related fields who will find the problems raised in learning theory close to their interests.
Inhaltsverzeichnis
Preface; Foreword; 1. The framework of learning; 2. Basic hypothesis spaces; 3. Estimating the sample error; 4. Polynomial decay approximation error; 5. Estimating covering numbers; 6. Logarithmic decay approximation error; 7. On the bias-variance problem; 8. Regularization; 9. Support vector machines for classification; 10. General regularized classifiers; Bibliography; Index.
Details
Erscheinungsjahr: 2015
Genre: Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Buch
ISBN-13: 9780521865593
ISBN-10: 052186559X
Sprache: Englisch
Ausstattung / Beilage: HC gerader Rücken kaschiert
Einband: Gebunden
Autor: Cucker, Felipe
Zhou, Ding Xuan
Hersteller: Cambridge University Press
Maße: 235 x 157 x 19 mm
Von/Mit: Felipe Cucker (u. a.)
Erscheinungsdatum: 10.02.2015
Gewicht: 0,545 kg
Artikel-ID: 102080228
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