| Record ID | marc_columbia/Columbia-extract-20221130-007.mrc:236865667:1701 |
| Source | marc_columbia |
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LEADER: 01701mam a22003374a 4500
001 3235046
005 20221020013728.0
008 010727t20022002maua b 001 0 eng
010 $a 2001044445
020 $a026208306X (hc. : alk. paper)
035 $a(OCoLC)ocm47705793
035 $9AUK7841CU
035 $a(NNC)3235046
035 $a3235046
040 $aDLC$cDLC$dOrLoB-B
042 $apcc
050 00 $aQ325.5$b.H48 2002
082 00 $a006.3/1$221
100 1 $aHerbrich, Ralf.$0http://id.loc.gov/authorities/names/n2001002360
245 10 $aLearning kernel classifiers :$btheory and algorithms /$cRalf Herbrich.
260 $aCambridge, Mass. :$bMIT Press,$c[2002], ©2002.
300 $axx, 364 pages :$billustrations ;$c24 cm.
336 $atext$btxt$2rdacontent
337 $aunmediated$bn$2rdamedia
490 1 $aAdaptive computation and machine learning
504 $aIncludes bibliographical references (p. [339]-355) and index.
505 00 $g1.$tIntroduction --$gI.$tLearning Algorithms.$g2.$tKernel Classifiers from a Machine Learning Perspective.$g3.$tKernel Classifiers from a Bayesian Perspective --$gII.$tLearning Theory.$g4.$tMathematical Models of Learning.$g5.$tBounds for Specific Algorithms --$gIII.$tAppendices --$gA.$tTheoretical Background and Basic Inequalities --$gB.$tProofs and Derivations - Part I --$gC.$tProofs and Derivations - Part II --$gD.$tPseudocodes.
650 0 $aMachine learning.$0http://id.loc.gov/authorities/subjects/sh85079324
650 0 $aAlgorithms.$0http://id.loc.gov/authorities/subjects/sh85003487
830 0 $aAdaptive computation and machine learning.$0http://id.loc.gov/authorities/names/n97066095
852 00 $boff,eng$hQ325.5$i.H48 2002