MARC Record from marc_columbia

LEADER: 03425fam a2200481 a 4500
001 4058228
005 20221027025545.0
008 970731t19981998maua b 001 0 eng
010 $a 97036102
020 $a026202442X (hc : alk. paper)
035 $a(OCoLC)37437670
035 $a(OCoLC)ocm37437670
035 $9ANQ4531HS
035 $a(NNC)4058228
035 $a4058228
040 $aDLC$cDLC$dDLC$dNNC-M$dOrLoB-B
050 00 $aQH506$b.B35 1998
082 00 $a572.8/01/13$221
100 1 $aBaldi, Pierre.$0http://id.loc.gov/authorities/names/n97077337
245 10 $aBioinformatics :$bthe machine learning approach /$cPierre Baldi, Søren Brunak.
260 $aCambridge, Mass. :$bMIT Press,$c[1998], ©1998.
300 $axviii, 351 pages :$billustrations (some color) ;$c24 cm.
336 $atext$btxt$2rdacontent
337 $aunmediated$bn$2rdamedia
490 1 $aAdaptive computation and machine learning
500 $a"A Bradford book."
504 $aIncludes bibliographical references (p. 319-346) and index.
505 00 $g1.$tIntroduction --$g2.$tMachine Learning Foundations: The Probabilistic Framework --$g3.$tProbabilistic Modeling and Inference: Examples --$g4.$tMachine Learning Algorithms --$g5.$tNeural Networks: The Theory --$g6.$tNeural Networks: Applications --$g7.$tHidden Markov Models: The Theory --$g8.$tHidden Markov Models: Applications --$g9.$tHybrid Systems: Hidden Markov Models and Neural Networks --$g10.$tProbabilistic Models of Evolution: Phylogenetic Trees --$g11.$tStochastic Grammars and Linguistics --$g12.$tInternet Resources and Public Databases --$gB.$tInformation Theory, Entropy, and Relative Entropy --$gC.$tProbabilistic Graphical Models --$gD.$tHMM Technicalities, Scaling, Periodic Architectures, State Functions, and Dirichlet Mixtures.
520 $aPierre Baldi and Soren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed at two types of researchers and students. First are the biologists and biochemists who need to understand new data-driven algorithms, such as neural networks and hidden Markov models, in the context of biological sequences and their molecular structure and function.
520 8 $aSecond are those with a primary background in physics, mathematics, statistics, or computer science who need to know more about specific applications in molecular biology.
650 0 $aMolecular biology$xComputer simulation.
650 0 $aMolecular biology$xMathematical models.
650 0 $aNeural networks (Computer science)$0http://id.loc.gov/authorities/subjects/sh90001937
650 0 $aMachine learning.$0http://id.loc.gov/authorities/subjects/sh85079324
650 0 $aMarkov processes.$0http://id.loc.gov/authorities/subjects/sh85081369
650 2 $aMolecular Biology.$0https://id.nlm.nih.gov/mesh/D008967
650 2 $aArtificial Intelligence.$0https://id.nlm.nih.gov/mesh/D001185
650 2 $aNeural Networks, Computer.$0https://id.nlm.nih.gov/mesh/D016571
650 2 $aModels, Theoretical.$0https://id.nlm.nih.gov/mesh/D008962
650 2 $aMarkov Chains.$0https://id.nlm.nih.gov/mesh/D008390
700 1 $aBrunak, Søren.$0http://id.loc.gov/authorities/names/n88170010
830 0 $aAdaptive computation and machine learning.$0http://id.loc.gov/authorities/names/n97066095
852 00 $boff,hsl$hQH506$i.B35 1998