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008			200423s2020 gw a o |||| 0|eng d
020			_a9783030378301
024	7		_a10.1007/978-3-030-37830-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2020 EB
245	0	0	_aImplementations and Applications of Machine Learning / _cedited by Saad Subair, Christopher Thron
250			_aFirst edition
264		1	_aCham _bSpringer International Publishing : _bImprint: Springer _c2020
300			_a1 recurso en línea (XII, 280 páginas) _b120 ilustraciones, 92 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aArchivo de texto _bPDF
490	0		_aStudies in Computational Intelligence _x1860-949X _v782
490	0		_aEngineering (Springer-11647)
505	0		_aIntroduction -- Part 1: Machine learning concepts, methods, and software tools -- Overview -- Classifying algorithms -- Support vector machines -- Bayes classifiers -- Decision trees -- Clustering algorithms -- k-means and variants -- Gaussian mixture -- Association rules -- Optimization algorithms -- Genetic algorithms -- Swarm intelligence -- Deep learning,- Convolutional neural networks (CNN) -- Other deep learning schema -- Part 2: Applications with implementations -- Protein secondary structure prediction -- Mapping heart disease risk -- Surgical performance monitoring -- Power grid control -- Conclusion.
520	3		_aThis book provides step-by-step explanations of successful implementations and practical applications of machine learning. The book's GitHub page contains software codes to assist readers in adapting materials and methods for their own use. A wide variety of applications are discussed, including wireless mesh network and power systems optimization; computer vision; image and facial recognition; protein prediction; data mining; and data discovery. Numerous state-of-the-art machine learning techniques are employed (with detailed explanations), including biologically-inspired optimization (genetic and other evolutionary algorithms, swarm intelligence); Viola Jones face detection; Gaussian mixture modeling; support vector machines; deep convolutional neural networks with performance enhancement techniques (including network design, learning rate optimization, data augmentation, transfer learning); spiking neural networks and timing dependent plasticity; frequent itemset mining; binary classification; and dynamic programming. This book provides valuable information on effective, cutting-edge techniques, and approaches for students, researchers, practitioners, and teachers in the field of machine learning. Presents practical, useful applications of machine learning for practitioners, students, and researchers Provides hands-on tools for a variety of machine learning techniques Covers evolutionary and swarm intelligence, facial and image recognition, deep learning, data mining and discovery, and statistical techniques.
988			_aSpringer_Engineering_23062020
650		7	_aAprendizaje automático _2embne _9166090
650		7	_aInteligencia artificial _2embne _9413115
700	1		_aSubair, Saad _eeditor _4edt _4http://id.loc.gov/vocabulary/relators/edt _1http://viaf.org/viaf/5427159156326812180006
700	1		_aThron, Christopher _eeditor _4edt _4http://id.loc.gov/vocabulary/relators/edt
710	2		_aSpringerLink (Online service) _0http://id.loc.gov/authorities/names/no2005046756 _1http://viaf.org/viaf/148105729
776	0	8	_iPrinted edition: _z9783030378295
776	0	8	_iPrinted edition: _z9783030378318
776	0	8	_iPrinted edition: _z9783030378325
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-37830-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b07/2020 _dz _eo _zSI