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Edité par Springer Nature Switzerland AG, 2021
ISBN 10 : 3030819345 ISBN 13 : 9783030819347
Langue: anglais
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EUR 64,76
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ISBN 10 : 3030819345 ISBN 13 : 9783030819347
Langue: anglais
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Ajouter au panierHardcover. Etat : Brand New. 3rd edition. 458 pages. 9.75x6.50x1.25 inches. In Stock.
Edité par Springer International Publishing, 2021
ISBN 10 : 3030819345 ISBN 13 : 9783030819347
Langue: anglais
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
EUR 64,19
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Ajouter au panierBuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.
Edité par Springer International Publishing, Springer International Publishing Sep 2021, 2021
ISBN 10 : 3030819345 ISBN 13 : 9783030819347
Langue: anglais
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
EUR 64,19
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Ajouter au panierBuch. Etat : Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 476 pp. Englisch.
Edité par Springer Nature Switzerland AG, CH, 2021
ISBN 10 : 3030819345 ISBN 13 : 9783030819347
Langue: anglais
Vendeur : Rarewaves.com UK, London, Royaume-Uni
EUR 81,67
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Ajouter au panierHardback. Etat : New. 3rd ed. 2021. 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.
Edité par Springer Nature Switzerland AG, CH, 2021
ISBN 10 : 3030819345 ISBN 13 : 9783030819347
Langue: anglais
Vendeur : Rarewaves.com USA, London, LONDO, Royaume-Uni
EUR 87,43
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Ajouter au panierHardback. Etat : New. 3rd ed. 2021. 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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Ajouter au panierHardcover. Etat : New. Brand New! Fast Delivery US Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 7-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.
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Ajouter au panierHardcover. Etat : Brand New. 3rd edition. 458 pages. 9.75x6.50x1.25 inches. In Stock.
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Ajouter au panierHardcover. Etat : Brand New. Brand New! Fast Delivery, Delivery within 7-12 working Day Only, USA Edition Original Edition. Excellent Quality, Printing in English Language, Quick delivery by FEDEX & DHL. USPS & UPS Act. Our courier service is not available at PO BOX& APO BOX. Ship from India & United States.
EUR 62,76
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Edité par Springer, Berlin|Springer International Publishing|Springer, 2021
ISBN 10 : 3030819345 ISBN 13 : 9783030819347
Langue: anglais
Vendeur : moluna, Greven, Allemagne
EUR 55,78
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Ajouter au panierEtat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 informatio.
Edité par Springer International Publishing Sep 2021, 2021
ISBN 10 : 3030819345 ISBN 13 : 9783030819347
Langue: anglais
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
EUR 64,19
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Ajouter au panierBuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 476 pp. Englisch.