Overview
- 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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Table of contents (6 chapters)
Keywords
About this book
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.
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Authors and Affiliations
About the authors
riggerRodrigo 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.Bibliographic Information
Book Title: Machine Learning
Book Subtitle: A Practical Approach on the Statistical Learning Theory
Authors: Rodrigo Fernandes de Mello, Moacir Antonelli Ponti
DOI: https://doi.org/10.1007/978-3-319-94989-5
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer International Publishing AG, part of Springer Nature 2018
Hardcover ISBN: 978-3-319-94988-8Published: 13 August 2018
Softcover ISBN: 978-3-030-06949-0Published: 01 February 2019
eBook ISBN: 978-3-319-94989-5Published: 01 August 2018
Edition Number: 1
Number of Pages: XV, 362
Number of Illustrations: 190 b/w illustrations
Topics: Artificial Intelligence, Probability and Statistics in Computer Science, Mathematical Applications in Computer Science, Applied Statistics