MARC Record from marc_columbia

LEADER: 05632cam a2200697 a 4500
001 14671488
005 20220514231823.0
006 m o d
007 cr cnu---unuuu
008 110607s2011 fluh ob 001 0 eng d
035 $a(OCoLC)ocn729371391
035 $a(NNC)14671488
040 $aN$T$beng$epn$cN$T$dYDXCP$dE7B$dOCLCQ$dFXR$dOHS$dOCLCQ$dUIU$dOCLCQ$dDEBSZ$dOCLCQ$dUIU$dUMI$dDEBBG$dOTZ$dOCLCF$dCRCPR$dIDEBK$dUA@$dOCLCQ$dVT2$dCEF$dCOO$dUWO$dYDX$dESU$dK6U$dOCLCO$dOCLCQ$dOCLCO
015 $aGBB0A8118$2bnb
016 7 $a015644732$2Uk
019 $a759865834$a880372993$a1008953846$a1067231608$a1077296510$a1084344055$a1156371789$a1192336320$a1240534068
020 $a9781439821282$q(electronic bk.)
020 $a1439821283$q(electronic bk.)
020 $z9781439821275
020 $z1439821275
024 7 $a10.1201/b10911$2doi
035 $a(OCoLC)729371391$z(OCoLC)759865834$z(OCoLC)880372993$z(OCoLC)1008953846$z(OCoLC)1067231608$z(OCoLC)1077296510$z(OCoLC)1084344055$z(OCoLC)1156371789$z(OCoLC)1192336320$z(OCoLC)1240534068
037 $aCL0500000429$bSafari Books Online
050 4 $aQ325.5$b.S866 2011eb
060 4 $aQU 26.5
072 7 $aSCI$x013020$2bisacsh
082 04 $a542/.85$222
084 $aCOM000000$aMED009000$aSCI013050$2bisacsh
049 $aZCUA
245 00 $aSupport vector machines and their application in chemistry and biotechnology /$cYizeng Liang [and others].
260 $aBoca Raton, FL :$bCRC Press,$c2011.
300 $a1 online resource (x, 193 pages)
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
520 $a"Support vector machines (SVMs), a promising machine learning method, is a powerful tool for chemical data analysis and for modeling complex physicochemical and biological systems. It is of growing interest to chemists and has been applied to problems in such areas as food quality control, chemical reaction monitoring, metabolite analysis, QSAR/QSPR, and toxicity. This book presents the theory of SVMs in a way that is easy to understand regardless of mathematical background. It includes simple examples of chemical and OMICS data to demonstrate the performance of SVMs and compares SVMs to other traditional classification/regression methods"--$cProvided by publisher
520 $a"Support vector machines (SVMs) seem a very promising kernel-based machine learning method originally developed for pattern recognition and later extended to multivariate regression. What distinguishes SVMs from traditional learning methods lies in its exclusive objective function, which minimizes the structural risk of the model. The introduction of the kernel function into SVMs made it extremely attractive, since it opens a new door for chemists/biologists to use SVMs to solve difficult nonlinear problems in chemistry and biotechnology through the simple linear transformation technique. The distinctive features and excellent empirical performances of SVMs have drawn the eyes of chemists and biologists so much that a number of papers, mainly concerned with the applications of SVMs, have been published in chemistry and biotechnology in recent years. These applications cover a large scope of chemical and/or biological meaningful problems, e.g. spectral calibration, drug design, quantitative structure-activity/property relationship (QSAR/QSPR), food quality control, chemical reaction monitoring, metabolic fingerprint analysis, protein structure and function prediction, microarray data-based cancer classification and so on. However, in order to efficiently apply this rather new technique to solve difficult problems in chemistry and biotechnology, one should have a sound in-depth understanding of what kind information this new mathematical tool could really provide and what its statistic property is. This book aims at giving a deeper and more thorough description of the mechanism of SVMs from the point of view of chemists/biologists and hence to make it easy for chemists and biologists to understand"--$cProvided by publisher
504 $aIncludes bibliographical references and index.
505 0 $ach. 1. Overview of support vector machines -- ch. 2. Support vector machines for classification and regression -- ch. 3. Kernel methods -- ch. 4. Ensemble learning of support vector machines -- ch. 5. Support vector machines applied to near-infrared spectroscopy -- ch. 6. Support vector machines and QSAR/QSPR -- ch. 7. Support vector machines applied to traditional Chinese medicine -- ch. 8. Support vector machines applied to OMICS study.
588 0 $aPrint version record.
650 0 $aSupport vector machines.
650 0 $aChemometrics.
650 0 $aChemistry.
650 0 $aLinear programming.
650 2 $aChemistry
650 2 $aBiotechnology
650 2 $aProgramming, Linear
650 6 $aMachines à vecteurs supports.
650 6 $aChimiométrie.
650 6 $aChimie.
650 6 $aBiotechnologie.
650 6 $aProgrammation linéaire.
650 7 $achemistry.$2aat
650 7 $abioengineering.$2aat
650 7 $aChemometrics.$2fast$0(OCoLC)fst01736550
650 7 $aSupport vector machines.$2fast$0(OCoLC)fst01747369
655 4 $aElectronic books.
700 1 $aLiang, Yizeng.
776 08 $iPrint version:$tSupport vector machines and their application in chemistry and biotechnology.$dBoca Raton, FL : CRC Press, 2011$z9781439821275$w(DLC) 2010041498$w(OCoLC)663445103
856 40 $uhttp://www.columbia.edu/cgi-bin/cul/resolve?clio14671488$zTaylor & Francis eBooks
852 8 $blweb$hEBOOKS