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An introduction to machine learning

Kubat, Miroslav.

An introduction to machine learning

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	An introduction to machine learningby Miroslav Kubat.
作者:	Kubat, Miroslav.
出版者:	Cham :Springer International Publishing :2021.
面頁冊數:	xviii, 458 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Machine learning.
電子資源:	https://doi.org/10.1007/978-3-030-81935-4
ISBN:	9783030819354$q(electronic bk.)

An introduction to machine learning
Kubat, Miroslav.

An introduction to machine learning[electronic resource] /by Miroslav Kubat. - Third edition. - Cham :Springer International Publishing :2021. - xviii, 458 p. :ill. (some col.), digital ;24 cm.

1. Ambitions and Goals of Machine Learning -- 2. Probabilities: Bayesian Classifiers -- 3. Similarities: Nearest-Neighbor Classifiers -- 4. Inter-Class Boundaries: Linear and Polynomial Classifiers -- 5. Decision Trees -- 6. Artificial Neural Networks -- 7. Computational Learning Theory -- 8. Experience from Historical Applications -- 9. Voting Assemblies and Boosting -- 10. Classifiers in the Form of Rule-Sets -- 11. Practical Issues to Know About -- 12. Performance Evaluation -- 13. Statistical Significance -- 14. Induction in Multi-Label Domains -- 15. Unsupervised Learning -- 16. Deep Learning -- 17. Reinforcement Learning: N-Armed Bandits and Episodes -- 18. Reinforcement Learning: From TD(0) to Deep-Q-Learning -- 19. Temporal Learning -- 20. Hidden Markov Models -- 21. Genetic Algorithm -- Bibliography -- Index.

This textbook offers a comprehensive introduction to Machine Learning techniques and algorithms. This Third Edition covers newer approaches that have become highly topical, including deep learning, and auto-encoding, introductory information about temporal learning and hidden Markov models, and a much more detailed treatment of reinforcement learning. The book is written in an easy-to-understand manner with many examples and pictures, and with a lot of practical advice and discussions of simple applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, rule-induction programs, artificial neural networks, support vector machines, boosting algorithms, unsupervised learning (including Kohonen networks and auto-encoding), deep learning, reinforcement learning, temporal learning (including long short-term memory), hidden Markov models, and the genetic algorithm. Special attention is devoted to performance evaluation, statistical assessment, and to many practical issues ranging from feature selection and feature construction to bias, context, multi-label domains, and the problem of imbalanced classes.

ISBN: 9783030819354$q(electronic bk.)

Standard No.: 10.1007/978-3-030-81935-4doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5 / .K83 2021

Dewey Class. No.: 006.3

An introduction to machine learning
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505 0 $a 1. Ambitions and Goals of Machine Learning -- 2. Probabilities: Bayesian Classifiers -- 3. Similarities: Nearest-Neighbor Classifiers -- 4. Inter-Class Boundaries: Linear and Polynomial Classifiers -- 5. Decision Trees -- 6. Artificial Neural Networks -- 7. Computational Learning Theory -- 8. Experience from Historical Applications -- 9. Voting Assemblies and Boosting -- 10. Classifiers in the Form of Rule-Sets -- 11. Practical Issues to Know About -- 12. Performance Evaluation -- 13. Statistical Significance -- 14. Induction in Multi-Label Domains -- 15. Unsupervised Learning -- 16. Deep Learning -- 17. Reinforcement Learning: N-Armed Bandits and Episodes -- 18. Reinforcement Learning: From TD(0) to Deep-Q-Learning -- 19. Temporal Learning -- 20. Hidden Markov Models -- 21. Genetic Algorithm -- Bibliography -- Index.
520 $a This textbook offers a comprehensive introduction to Machine Learning techniques and algorithms. This Third Edition covers newer approaches that have become highly topical, including deep learning, and auto-encoding, introductory information about temporal learning and hidden Markov models, and a much more detailed treatment of reinforcement learning. The book is written in an easy-to-understand manner with many examples and pictures, and with a lot of practical advice and discussions of simple applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, rule-induction programs, artificial neural networks, support vector machines, boosting algorithms, unsupervised learning (including Kohonen networks and auto-encoding), deep learning, reinforcement learning, temporal learning (including long short-term memory), hidden Markov models, and the genetic algorithm. Special attention is devoted to performance evaluation, statistical assessment, and to many practical issues ranging from feature selection and feature construction to bias, context, multi-label domains, and the problem of imbalanced classes.
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