An Introduction to Machine Learning / by Miroslav Kubat.

Format:

Book

Author/Creator:

Kubát, Miroslav, author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Language:

English

Subjects (All):

Data mining.

Artificial intelligence.

Big data.

Computational intelligence.

Data Mining and Knowledge Discovery.

Artificial Intelligence.

Big Data/Analytics.

Computational Intelligence.

Local Subjects:

Data Mining and Knowledge Discovery.

Artificial Intelligence.

Big Data/Analytics.

Computational Intelligence.

Physical Description:

1 online resource (XIII, 348 pages) : 85 illustrations, 3 illustrations in color

Edition:

Second edition 2017.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2017.

System Details:

text file PDF

Summary:

This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant 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. This revised edition contains three entirely new chapters on critical topics regarding the pragmatic application of machine learning in industry. The chapters examine multi-label domains, unsupervised learning and its use in deep learning, and logical approaches to induction as well as Inductive Logic Programming. Numerous chapters have been expanded, and the presentation of the material has been enhanced. The book contains many new exercises, numerous solved examples, thought-provoking experiments, and computer assignments for independent work.

Contents:

1 A Simple Machine-Learning Task

2 Probabilities: Bayesian Classifiers

Similarities: Nearest-Neighbor Classifiers

4 Inter-Class Boundaries: Linear and Polynomial Classifiers

5 Artificial Neural Networks

6 Decision Trees

7 Computational Learning Theory

8 A Few Instructive Applications

9 Induction of Voting Assemblies

10 Some Practical Aspects to Know About

11 Performance Evaluation

12 Statistical Significance

13 Induction in Multi-Label Domains

14 Unsupervised Learning

15 Classifiers in the Form of Rulesets

16 The Genetic Algorithm

17 Reinforcement Learning.

Other Format:

Printed edition:

ISBN:

978-3-319-63913-0

9783319639130

Access Restriction:

Restricted for use by site license.