000			02848nam a22004335i 4500
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003			ES-MaUEC
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007			cr nn 008mamaa
008			230211s2011 sz | s |||| 0|eng d
020			_a9783031015526
024	7		_a10.1007/978-3-031-01552-6 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2011 EB
100	1		_aCampbell, Colin _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686719
245	1	0	_aLearning with Support Vector Machines _cby Colin Campbell, Yiming Ying
250			_a1st edition 2011
264		1	_aCham _bSpringer International Publishing _c2011
300			_a1 recurso en línea (X, 83 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Artificial Intelligence and Machine Learning _x1939-4616
505	0		_aSupport Vector Machines for Classification -- Kernel-based Models -- Learning with Kernels.
520			_aSupport Vectors Machines have become a well established tool within machine learning. They work well in practice and have now been used across a wide range of applications from recognizing hand-written digits, to face identification, text categorisation, bioinformatics, and database marketing. In this book we give an introductory overview of this subject. We start with a simple Support Vector Machine for performing binary classification before considering multi-class classification and learning in the presence of noise. We show that this framework can be extended to many other scenarios such as prediction with real-valued outputs, novelty detection and the handling of complex output structures such as parse trees. Finally, we give an overview of the main types of kernels which are used in practice and how to learn and make predictions from multiple types of input data. Table of Contents: Support Vector Machines for Classification / Kernel-based Models / Learning with Kernels.
988			_aSynthesis Collection of Technology_2011
650		7	_2embne _9166090 _aAprendizaje automático
650		7	_2embne _9678664 _aRedes neuronales artificiales
650		7	_2embne _9140833 _aSistemas operativos
700	1		_aYing, Yiming _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686720
776	0	8	_iPrinted edition: _z9783031004247
776	0	8	_iPrinted edition: _z9783031026805
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01552-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2023 _dz _eIG _zSI