MARC Record from Library of Congress

LEADER: 03387cam a22003134a 4500
001 2010041498
003 DLC
005 20120419081527.0
008 101028s2011 flua b 001 0 eng
010 $a 2010041498
015 $aGBB0A8118$2bnb
016 7 $a015644732$2Uk
020 $a9781439821275 (hardback)
020 $a1439821275 (hardback)
035 $a(OCoLC)ocn663445103
040 $aDLC$cDLC$dYDX$dBTCTA$dYDXCP$dUKM$dDLC
042 $apcc
050 00 $aQ325.5$b.S866 2011
082 00 $a542/.85$222
084 $aCOM000000$aMED009000$aSCI013050$2bisacsh
245 00 $aSupport vector machines and their application in chemistry and biotechnology /$cYizeng Liang ... [et al.].
260 $aBoca Raton :$bCRC Press,$cc2011.
300 $ax, 201 p. :$bill. ;$c24 cm.
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.
650 0 $aSupport vector machines.
650 0 $aChemometrics.
700 1 $aLiang, Yizeng.