| Record ID | marc_columbia/Columbia-extract-20221130-013.mrc:174152559:2830 |
| Source | marc_columbia |
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LEADER: 02830cam a22003614a 4500
001 6200860
005 20221122004514.0
008 061228t20072007paua b 001 0 eng
010 $a 2006041348
020 $a9780898716269 (pbk. : alk. paper)
020 $a0898716268 (pbk. : alk. paper)
035 $a(OCoLC)ocm77572489
035 $a(OCoLC)77572489
035 $a(NNC)6200860
035 $a6200860
040 $aDLC$cDLC$dBTCTA$dBAKER$dC#P$dOrLoB-B
050 00 $aQA76.9.D343$bE52 2007
082 00 $a05.74$2220
100 1 $aEldén, Lars,$d1944-$0http://id.loc.gov/authorities/names/n89668941
245 10 $aMatrix methods in data mining and pattern recognition /$cLars Eldén.
260 $aPhiladelphia, PA :$bSociety for Industrial and Applied Mathematics,$c[2007], ©2007.
300 $ax, 224 pages :$billustrations ;$c26 cm.
336 $atext$btxt$2rdacontent
337 $aunmediated$bn$2rdamedia
490 1 $aFundamentals of algorithms
504 $aIncludes bibliographical references (p. 209-216) and index.
505 00 $gI.$tLinear algebra concepts and matrix decompositions -- $g1.$tVectors and matrices in data mining and pattern recognition -- $g2.$tVectors and matrices -- $g3.$tLinear systems and least squares -- $g4.$tOrthogonality -- $g5.$tQR decomposition -- $g6.$tSingular value decomposition -- $g7.$tReduced-rank least squares models -- $g8.$tTensor decomposition -- $gII.$tData mining applications -- $g10.$tClassification of handwritten digits -- $g11.$tText mining -- $g12.$tPage ranking for a Web search engine -- $g13.$tAutomatic key word and key sentence extraction -- $g14.$tFace recognition using tensor SVD -- $gIII.$tComputing the matrix decompositions -- $g15.$tComputing eigenvalues and singular values.
520 1 $a"Several very powerful numerical linear algebra techniques are available for solving problems in data mining and pattern recognition. This application-oriented book describes how modern matrix methods can be used to solve these problems, gives an introduction to matrix theory and decompositions, and provides students with a set of tools that can be modified for a particular application." "The book is intended for undergraduate students who have previously taken an introductory scientific computing/numerical analysis course. Graduate students in various data mining and pattern recognition areas who need an introduction to linear algebra techniques will also find the book useful."--BOOK JACKET.
650 0 $aData mining.$0http://id.loc.gov/authorities/subjects/sh97002073
650 0 $aPattern recognition systems$xMathematical models.
650 0 $aAlgebras, Linear.$0http://id.loc.gov/authorities/subjects/sh85003441
830 0 $aFundamentals of algorithms.$0http://id.loc.gov/authorities/names/n2003009311
852 00 $boff,eng$hQA76.9.D343$iE52 2007