000			02964nam a22004455i 4500
001			u370579
003			SIRSI
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007			cr nn 008mamaa
008			110517s2011 xxk| s |||| 0|eng d
020			_a9780857295255 _9978-0-85729-525-5
040			_cMX-MeUAM
050		4	_aQA76.9.D343
082	0	4	_a006.312 _223
100	1		_aVeloso, Adriano. _eauthor.
245	1	0	_aDemand-Driven Associative Classification _h[recurso electrónico] / _cby Adriano Veloso, Wagner Meira Jr.
264		1	_aLondon : _bSpringer London, _c2011.
300			_aXIII, 112p. 27 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSpringerBriefs in Computer Science, _x2191-5768
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.
650		0	_aComputer science.
650		0	_aData mining.
650	1	4	_aComputer Science.
650	2	4	_aData Mining and Knowledge Discovery.
650	2	4	_aProbability and Statistics in Computer Science.
700	1		_aMeira Jr., Wagner. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780857295248
830		0	_aSpringerBriefs in Computer Science, _x2191-5768
856	4	0	_zLibro electrónico _uhttp://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-0-85729-525-5
596			_a19
942			_cLIBRO_ELEC
999			_c198459 _d198459