000			03117nam a22004815i 4500
003			DE-He213
005			20191011024704.0
007			cr nn 008mamaa
008			110517s2011 xxk| s |||| 0|eng d
020	6	4	_a9780857295255 _9978-0-85729-525-5
024	8	7	_a10.1007/978-0-85729-525-5 _2doi
050	8	4	_aQA76.9.D343
072	8	7	_aUNF _2bicssc
072	8	7	_aUYQE _2bicssc
072	8	7	_aCOM021030 _2bisacsh
082			_a006.312 _223
100	8	1	_aVeloso, Adriano. _eauthor. _934341
245	9	7	_aDemand-Driven Associative Classification _h[electronic resource] / _cby Adriano Veloso, Wagner Meira Jr.
001			000048140
300	6	4	_aXIII, 112p. 27 illus. _bonline resource.
490	8	1	_aSpringerBriefs in Computer Science, _x2191-5768
505	8	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	6	4	_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.
650	8	0	_aComputer science. _934342
650	8	0	_aData mining. _98942
650			_aComputer Science. _934343
650			_aData Mining and Knowledge Discovery. _98949
650			_aProbability and Statistics in Computer Science. _98948
700	8	1	_aMeira Jr., Wagner. _eauthor. _934344
710	8	2	_aSpringerLink (Online service) _934345
773	8	0	_tSpringer eBooks
776			_iPrinted edition: _z9780857295248
830	8	0	_aSpringerBriefs in Computer Science, _x2191-5768 _934346
856			_uhttp://dx.doi.org/10.1007/978-0-85729-525-5 _zde clik aquí para ver el libro electrónico
264	8	1	_aLondon : _bSpringer London, _c2011.
336	6	4	_atext _btxt _2rdacontent
337	6	4	_acomputer _bc _2rdamedia
338	6	4	_aonline resource _bcr _2rdacarrier
347	6	4	_atext file _bPDF _2rda
516	6	4	_aZDB-2-SCS
999			_c47869 _d47869
942			_c05