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003			ES-MaUEC
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007			cr nn nnnaamaa
008			210925s2021 sz | s |||| 0|eng d
020			_a9783030819354
024	7		_a10.1007/978-3-030-81935-4 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2021 EB
100	1		_aKubat, Miroslav _eautor _9682227 _d1958-
245	1	3	_aAn Introduction to Machine Learning _cby Miroslav Kubat
250			_aThrid edition 2021
264		1	_aCham _bSpringer International Publising _c2021
300			_a1 recurso en línea (XVIII, 458 páginas) _b114 ilustraciones, 5 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aComputer Science (SpringerNature-11645)
490	0		_aComputer Science (R0) (SpringerNature-43710)
505	0		_a1. 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	3		_aThis 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.
988			_aSpringer_Computer_2021
650		7	_2embne _9166090 _aAprendizaje automático
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-81935-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2022 _dz _eu _zSI