Machine Learning

A Practical Approach on the Statistical Learning Theory

Authors: F MELLO, RODRIGO, Ponti, Moacir Antonelli

  • This book includes a relevant discussion on Classification Algorithms as well as their source codes using the R Statistical Language
  • It also presents a very simple approach to understand the Statistical Learning Theory, which is considered a complex subject
  • Finally, it also discusses Kernels in a very user-friendly fashion
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eBook $84.99
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  • ISBN 978-3-319-94989-5
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  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $109.99
price for USA in USD
  • ISBN 978-3-319-94988-8
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
About this Textbook

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.   

About the authors

Rodrigo Fernandes de Mello is Associate Professor with the Department of Computer Science, at the Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos, SP, Brazil. He obtained his PhD degree from the University of São Paulo. His research interests include the Statistical Learning Theory, Machine Learning, Data Streams, and Applications in Dynamical Systems concepts. He has published more than 100 papers including journals and conferences, supported and organized international conferences, besides serving as Editor of International Journals.

Moacir Antonelli Ponti is Associate Professor with the Department of Computer Science, at the Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos, Brazil, and was visiting researcher at the Centre for Vision, Speech and Signal Processing (CVSSP), University of Surrey. He obtained his PhD from the Federal University of São Carlos. His research interests include Pattern Recognition and Computer Vision, as well as Signal, Image and Video Processing.


Table of contents (6 chapters)

  • A Brief Review on Machine Learning

    Fernandes de Mello, Rodrigo (et al.)

    Pages 1-74

  • Statistical Learning Theory

    Fernandes de Mello, Rodrigo (et al.)

    Pages 75-128

  • Assessing Supervised Learning Algorithms

    Fernandes de Mello, Rodrigo (et al.)

    Pages 129-161

  • Introduction to Support Vector Machines

    Fernandes de Mello, Rodrigo (et al.)

    Pages 163-226

  • In Search for the Optimization Algorithm

    Fernandes de Mello, Rodrigo (et al.)

    Pages 227-324

Buy this book

eBook $84.99
price for USA in USD (gross)
  • ISBN 978-3-319-94989-5
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $109.99
price for USA in USD
  • ISBN 978-3-319-94988-8
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Machine Learning
Book Subtitle
A Practical Approach on the Statistical Learning Theory
Authors
Copyright
2018
Publisher
Springer International Publishing
Copyright Holder
Springer International Publishing AG, part of Springer Nature
eBook ISBN
978-3-319-94989-5
DOI
10.1007/978-3-319-94989-5
Hardcover ISBN
978-3-319-94988-8
Edition Number
1
Number of Pages
XV, 362
Number of Illustrations
190 b/w illustrations
Topics