An Introduction to Machine Learning

Kubat, Miroslav

ISBN 10: 3319200097 / ISBN 13: 9783319200095

Published by Springer (edition 1st ed. 2015), 2015

About this Item

It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

Seller Inventory # 3319200097-11-1

Bibliographic Details

• Title: An Introduction to Machine Learning
• Publisher: Springer (edition 1st ed. 2015)
• Publication Date: 2015
• Language: English
• ISBN 10: 3319200097
• ISBN 13: 9783319200095
• Binding: Hardcover
• Edition: 1st ed. 2015.
• Condition: Very Good

About this title

Synopsis

This book presents basic ideas of machine learning in a way that is easy to understand, by providing hands-on practical advice, using simple examples, and motivating students with discussions of interesting applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way of “boosting,” how to exploit them in more complicated domains, and how to deal with diverse advanced practical issues. One chapter is dedicated to the popular genetic algorithms.

About the Author

Miroslav Kubat, Associate Professor at the University of Miami, has been teaching and studying machine learning for more than a quarter century. Over the years, he has published more than 100 peer-reviewed papers, co-edited two books, served on the program committees of some 60 program conferences and workshops, and is the member of the editorial boards of three scientific journals. He is widely credited for having co-pioneered research in two major branches of the discipline: induction of time-varying concepts and learning from imbalanced training sets. Apart from that, he contributed to induction from multi-label examples, induction of hierarchically organized classes, genetic algorithms, initialization of neural networks, and other problems.