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008			150903s2011 xxk| o |||| 0|eng d
020			_a9780857295255 _99780857295255
024	7		_a10.1007/9780857295255 _2doi
035			_avtls000333914
039		9	_a201509030242 _bVLOAD _c201404130558 _dVLOAD _c201404092347 _dVLOAD _y201402041135 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aQA76.9.D343
100	1		_aVeloso, Adriano. _eautor _9306573
245	1	0	_aDemand-Driven Associative Classification / _cby Adriano Veloso, Wagner Meira Jr.
264		1	_aLondon : _bSpringer London, _c2011.
300			_axiii, 112 páginas 27 ilustraciones _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
490	0		_aSpringerBriefs in Computer Science, _x2191-5768
500			_aSpringer eBooks
505	0		_aIntroduction and Preliminaries -- Introduction -- The Classification Problem -- Associative Classification -- Demand-Driven Associative Classification -- Extensions to Associative Classification -- Multi-Label Associative Classification -- Competence-Conscious Associative Classification -- Calibrated Associative Classification -- Self-Training Associative Classification -- Ordinal Regression and Ranking --  Conclusions and FutureWork.
520			_aThe ultimate goal of machines is to help humans to solve problems. Such problems range between two extremes: structured problems for which the solution is totally defined (and thus are easily programmed by humans), and random problems for which the solution is completely undefined (and thus cannot be programmed). Problems in the vast middle ground have solutions that cannot be well defined and are, thus, inherently hard to program. Machine Learning is the way to handle this vast middle ground, so that many tedious and difficult hand-coding tasks would be replaced by automatic learning methods. There are several machine learning tasks, and this work is focused on a major one, which is known as classification. Some classification problems are hard to solve, but we show that they can be decomposed into much simpler sub-problems. We also show that independently solving these sub-problems by taking into account their particular demands, often leads to improved classification performance.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aMeira Jr., Wagner. _eautor _9306574
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9780857295248
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-0-85729-525-5 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999			_c281140 _d281140