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  3. Matematik til informatikfag

An Introduction to Machine Learning

Af: Miroslav Kubat Engelsk Hardback

An Introduction to Machine Learning

Af: Miroslav Kubat Engelsk Hardback
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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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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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Se mere i:
Produktdetaljer
Sprog: Engelsk
Sider: 458
ISBN-13: 9783030819347
Indbinding: Hardback
Udgave:
ISBN-10: 3030819345
Kategori: Matematik til informatikfag
Udg. Dato: 27 sep 2021
Længde: 35mm
Bredde: 270mm
Højde: 231mm
Forlag: Springer Nature Switzerland AG
Oplagsdato: 27 sep 2021
Forfatter(e): Miroslav Kubat
Forfatter(e) Miroslav Kubat

Kategori Matematik til informatikfag

ISBN-13 9783030819347

Sprog Engelsk

Indbinding Hardback

Sider 458

Udgave

Længde 35mm

Bredde 270mm

Højde 231mm

Udg. Dato 27 sep 2021

Oplagsdato 27 sep 2021

Forlag Springer Nature Switzerland AG

Kategori sammenhænge