Computational Learning Theory
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Computational Learning Theory
Author: Martin Anthony
language: en
Publisher: Cambridge University Press
Release Date: 1997-02-27
Computational learning theory is a subject which has been advancing rapidly in the last few years. The authors concentrate on the probably approximately correct model of learning, and gradually develop the ideas of efficiency considerations. Finally, applications of the theory to artificial neural networks are considered. Many exercises are included throughout, and the list of references is extensive. This volume is relatively self contained as the necessary background material from logic, probability and complexity theory is included. It will therefore form an introduction to the theory of computational learning, suitable for a broad spectrum of graduate students from theoretical computer science and mathematics.
Computational Learning Theory
Author: Jyrki Kivinen
language: en
Publisher: Springer Science & Business Media
Release Date: 2002-06-26
This book is tailored for students and professionals as well as novices from other fields to mass spectrometry. It will guide them from the basics to the successful application of mass spectrometry in their daily research. Starting from the very principles of gas-phase ion chemistry and isotopic properties, it leads through the design of mass analyzers and ionization methods in use to mass spectral interpretation and coupling techniques. Step by step the readers will learn how mass spectrometry works and what it can do as a powerful tool in their hands. The book comprises a balanced mixture of practice-oriented information and theoretical background. The clear layout, a wealth of high-quality figures and a database of exercises and solutions, accessible via the publisher's web site, support teaching and learning.
Computational Learning Theory
Author: Shai Ben-David
language: en
Publisher: Springer Science & Business Media
Release Date: 1997-03-03
Content Description #Includes bibliographical references and index.