000 03002nam a22004695i 4500
001 978-0-85729-525-5
003 DE-He213
005 20170628033425.0
007 cr nn 008mamaa
008 110517s2011 xxk| s |||| 0|eng d
020 _a9780857295255
_9978-0-85729-525-5
024 7 _a10.1007/978-0-85729-525-5
_2doi
050 4 _aQA76.9.D343
072 7 _aUNF
_2bicssc
072 7 _aUYQE
_2bicssc
072 7 _aCOM021030
_2bisacsh
082 0 4 _a006.312
_223
100 1 _aVeloso, Adriano.
_eauthor.
245 1 0 _aDemand-Driven Associative Classification
_h[electronic resource] /
_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 _uhttp://dx.doi.org/10.1007/978-0-85729-525-5
912 _aZDB-2-SCS
999 _c15120
_d15120