000			03582nam a22004215i 4500
001			86221
003			ES-MaUEC
005			20240611040146.0
007			cr nn 008mamaa
008			160809s2016 gw | s |||| 0|eng d
020			_a9783319411118
040			_aES-MaUEC _bspa
050		4	_aQA76.9.D343 _bH47 2016 EB
082	0	4	_a006.312
100	1		_aHerrera, Francisco. _945432 _0http://id.loc.gov/authorities/names/n85336219 _1http://viaf.org/viaf/18501604
245	1	0	_aMultilabel Classification : _bProblem Analysis, Metrics and Techniques _cby Francisco Herrera, Francisco Charte, Antonio J Rivera, María J del Jesus
264		1	_aCham _bSpringer International Publishing _c2016
300			_a1 recurso en línea (XVI, 194 páginas) _b72 ilustraciones
336			_aTexto (visual) _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
505	0		_aIntroduction -- Multilabel Classification -- Case Studies and Metrics -- Transformation based Classifiers -- Adaptation based Classifiers -- Ensemble based Classifiers -- Dimensionality Reduction -- Imbalance in Multilabel Datasets -- Multilabel Software.
520			_aThis book offers a comprehensive review of multilabel techniques widely used to classify and label texts, pictures, videos and music in the Internet. A deep review of the specialized literature on the field includes the available software needed to work with this kind of data. It provides the user with the software tools needed to deal with multilabel data, as well as step by step instruction on how to use them. The main topics covered are: � The special characteristics of multi-labeled data and the metrics available to measure them. � The importance of taking advantage of label correlations to improve the results. � The different approaches followed to face multi-label classification. � The preprocessing techniques applicable to multi-label datasets. � The available software tools to work with multi-label data. This book is beneficial for professionals and researchers in a variety of fields because of the wide range of potential applications for multilabel classification. Besides its multiple applications to classify different types of online information, it is also useful in many other areas, such as genomics and biology. No previous knowledge about the subject is required. The book introduces all the needed concepts to understand multilabel data characterization, treatment and evaluation.
650		7	_aData mining _2embne _9162648
650		7	_aInteligencia artificial _2embne _9413115
700	1		_aCharte Ojeda, Francisco _0Local _0http://id.loc.gov/authorities/names/no00022862 _1http://viaf.org/viaf/6932501 _9532
700	1		_aDel Jesus, María J. _0Local _999781
700	1		_aRivera, Antonio J. _999780 _0Local _1http://viaf.org/viaf/2117147665861860670001
710	2		_aSpringerLink (Online service) _0Local _0http://id.loc.gov/authorities/names/no2005046756 _1http://viaf.org/viaf/148105729 _9106996
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-319-41111-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
901			_ai9783319411118
907			_a.b12954998 _b10-10-17 _c21-11-16
942			_2lcc _cLE
945			_aEBOOK EB _g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i11615151 _z30-06-17
988	0	0	_aEBOOK, EBSPRINGER, GOBI_sep2018
988			_aEbook_one2one
998			_aSI _a_alco _a_vill _b10/2018 _cm _dz _ek _feng _ggw _h0
999			_c86221 _d86221 _x1