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008			210212s2021 gw a o |||| 0|eng d
020			_a9783030659004
024	7		_a10.1007/978-3-030-65900-4 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _erda _dES-MaUEC
050		4	_aQ325.5 _b2021 EB
100	1		_aJo, Taeho _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9673534
245	1	0	_aMachine Learning Foundations : _bSupervised, Unsupervised, and Advanced Learning _cby Taeho Jo.
250			_aFirst edition 2021
264		1	_aCham _bSpringer International Publishing _c2021
300			_a1 recurso en línea (XX, 391 páginas) _b277 ilustraciones, 13 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aEngineering (SpringerNature-11647)
490	0		_aEngineering (R0) (SpringerNature-43712)
505	0		_aPart I. Foundation -- Chapter 1. Introduction -- Chapter 2. Numerical Vectors -- Chapter 3.Data Encoding -- Chapter 4. Simple Machine Learning Algorithms -- Part II. Supervised Learning -- Chapter 5. Instance based Learning -- Chapter 6. Probabilistic Learning -- Chapter 7. Decision Tree -- Chapter 8. Support Vector Machine -- Part III. Unsupervised Learning -- Chapter 9. Simple Clustering Algorithms -- Chapter 10. K Means Algorithm -- Chapter 11. EM Algorithm -- Chapter 12. Advanced Clustering -- Part IV. Advanced Topics -- Chapter 13. Ensemble Learning -- Chapter 14. Semi-Supervised Learning -- Chapter 15. Temporal Learning -- Chapter 16. Reinforcement Learning.
520	3		_aThis book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning techniques. The book consists of four parts: foundation, supervised learning, unsupervised learning, and advanced learning. The first part provides the fundamental materials, background, and simple machine learning algorithms, as the preparation for studying machine learning algorithms. The second and the third parts provide understanding of the supervised learning algorithms and the unsupervised learning algorithms as the core parts. The last part provides advanced machine learning algorithms: ensemble learning, semi-supervised learning, temporal learning, and reinforced learning. Provides comprehensive coverage of both learning algorithms: supervised and unsupervised learning; Outlines the computation paradigm for solving classification, regression, and clustering; Features essential techniques for building the a new generation of machine learning.
988			_aSpringer_Engineering_2021
650		7	_2embne _aAprendizaje automático _9166090
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-65900-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE _n0
998			_b04/2021 _dz _eb _zSI