| Record ID | marc_columbia/Columbia-extract-20221130-011.mrc:3750455:1929 |
| Source | marc_columbia |
| Download Link | /show-records/marc_columbia/Columbia-extract-20221130-011.mrc:3750455:1929?format=raw |
LEADER: 01929cam a2200313Ia 4500
001 5003624
005 20221109204747.0
008 041027t20042004maua b 001 0 eng d
010 $a 2004109627
020 $a0262012111
035 $a(OCoLC)ocm56830710
035 $a(NNC)5003624
035 $a5003624
040 $aTEF$cTEF$dOrLoB-B
050 4 $aQ325.5$b.A473 2004
082 04 $a006.3/1$222
100 1 $aAlpaydin, Ethem.$0http://id.loc.gov/authorities/names/no2004104548
245 10 $aIntroduction to machine learning /$cEthem Alpaydın.
260 $aCambridge, Mass. :$bMIT Press,$c[2004], ©2004.
300 $axxx, 415 pages :$billustrations ;$c24 cm.
336 $atext$btxt$2rdacontent
337 $aunmediated$bn$2rdamedia
490 1 $aAdaptive computation and machine learning
504 $aIncludes bibliographical references and index.
505 00 $g1.$tIntroduction -- $g2.$tSupervised learning -- $g3.$tBayesian decision theory -- $g4.$tParametric methods -- $g5.$tMultivariate methods -- $g6.$tDimensionality reduction -- $g7.$tClustering -- $g8.$tNonparametric methods -- $g9.$tDecision trees -- $g10.$tLinear discrimination -- $g11.$tMultilayer perceptrons -- $g12.$tLocal models -- $g13.$tHidden Markov models -- $g14.$tAssessing and comparing classification algorithms -- $g15.$tCombining multiple learners -- $g16.$tReinforcement learning -- $gA.$tProbability.
520 1 $a"This book can be used by advanced undergraduates and graduate students who have completed courses in computer programming, probability, calculus, and linear algebra. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods."--BOOK JACKET.
650 0 $aMachine learning.$0http://id.loc.gov/authorities/subjects/sh85079324
830 0 $aAdaptive computation and machine learning.$0http://id.loc.gov/authorities/names/n97066095
852 00 $bsci$hQ325.5$i.A473 2004g