Support Vector Machine in Chemistry

By: Nianyi Chen, Wencong Lu, Jie Yang, Guozheng Li

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Hardcover | 1 December 2003

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344 Pages

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23.2 x 16.3 x 2.3

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In recent years, the support vector machine (SVM), a new data processing method, has been applied to many fields of chemistry and chemical technology. Compared with some other data processing methods, SVM is especially suitable for solving problems of small sample size, with superior prediction performance. SVM is fast becoming a powerful tool of chemometrics. This book provides a systematic approach to the principles and algorithms of SVM, and demonstrates the application examples of SVM in QSAR/QSPR work, materials and experimental design, phase diagram prediction, modeling for the optimal control of chemical industry, and other branches in chemistry and chemical technology.

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Introduction	p. 1
Support vector machine	p. 24
Kernel functions	p. 53
Feature selection using support vector machine	p. 60
Principle of atomic or molecular parameter-data processing method	p. 74
SVM applied to phase diagram assessment and prediction	p. 106
SVM applied to thermodynamic property prediction	p. 145
SVM applied to molecular and materials design	p. 156
SVM applied to structure-activity relationships	p. 186
SVM applied to data of trace element analysis	p. 220
SVM applied to archeological chemistry of ancient ceramics	p. 229
SVM applied to cancer research	p. 248
SVM applied to some topics of chemical analysis	p. 261
SVM applied to chemical and metallurgical technology	p. 271
The implementation of SVM	p. 305
Table of Contents provided by Blackwell. All Rights Reserved.

Support Vector Machine in Chemistry

At a Glance

Hardcover

Shipping

How to return your order

Defective items

You Can Find This Book In

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