000			03534nam a22003254a 4500
005			20250918153254.0
008			120425s2011 flua b 001 0 eng
020			_a9781439821275 (hbk.) _cRM369.55
020			_a1439821275 (hbk.)
039		9	_a201205231202 _brosli _c201205171604 _drahah _c201204250906 _drahah _y04-25-2012 _zrahah
040			_aDLC _cDLC _dYDX _dBTCTA _dYDXCP _dUKM _dDLC _dUKM
090			_aQ325.5.S836
090			_aQ325.5 _b.S836
245	0	0	_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.
504			_aIncludes bibliographical references and index.
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.
650		0	_aSupport vector machines.
650		0	_aChemometrics.
700	1		_aLiang, Yizeng.
907			_a.b15341252 _b2021-05-28 _c2019-11-12
942			_c01 _n0 _kQ325.5.S836
914			_avtls003498368
990			_ark4
991			_aFakulti Sains & Teknologi
998			_at _b2012-12-04 _cm _da _feng _gflu _y0 _z.b15341252
999			_c517897 _d517897