Synopsis
Offers frequent opportunities to practice techniques with control questions, exercises, thought experiments, and computer assignments.
Reinforces principles using well-selected toy domains and relevant real-world applications.
Provides additional material, including an instructor's manual with presentation slides, as well as answers to exercises.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
À propos de l?auteur
Miroslav Kubat, Associate Professor at the University of Miami, has been teaching and studying machine learning for over 25 years. He has published more than 100 peer-reviewed papers, co-edited two books, served on the program committees of over 60 conferences and workshops, and is an editorial board member of three scientific journals. He is widely credited with co-pioneering research in two major branches of the discipline: induction of time-varying concepts and learning from imbalanced training sets. He also contributed to research in induction from multi-label examples, induction of hierarchically organized classes, genetic algorithms, and initialization of neural networks.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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An Introduction to Machine Learning
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Springer, 2018
ISBN 10 : 3319876694
ISBN 13 : 9783319876696
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An Introduction to Machine Learning
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Springer, 2018
ISBN 10 : 3319876694
ISBN 13 : 9783319876696
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An Introduction to Machine Learning
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Springer International Publishing Aug 2018, 2018
ISBN 10 : 3319876694
ISBN 13 : 9783319876696
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 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. 364 pp. Englisch. N° de réf. du vendeur 9783319876696
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An Introduction to Machine Learning
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An Introduction to Machine Learning
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ISBN 10 : 3319876694
ISBN 13 : 9783319876696
Neuf
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Miroslav Kubat, Associate Professor at the University of Miami, has been teaching and studying machine learning for over 25 years. He has published more than 100 peer-reviewed papers, co-edited two books, served on the program committees of over 60 confe. N° de réf. du vendeur 448760880
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An Introduction to Machine Learning
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Taschenbuch. Etat : Neu. An Introduction to Machine Learning | Miroslav Kubat | Taschenbuch | Previously published in hardcover | xiii | Englisch | 2018 | Springer | EAN 9783319876696 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. N° de réf. du vendeur 115378954
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An Introduction to Machine Learning
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Springer, Berlin, Springer International Publishing, Springer, 2018
ISBN 10 : 3319876694
ISBN 13 : 9783319876696
Neuf
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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. 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. N° de réf. du vendeur 9783319876696
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