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E-raamat: Support Vector Machines and Their Application in Chemistry and Biotechnology

(Central South University, Changsha, China), (Central South University, Changsha, China), (Central South University, Changsha, China), (Central South University, Changsha, China)
  • Formaat: 211 pages
  • Ilmumisaeg: 19-Apr-2016
  • Kirjastus: CRC Press Inc
  • ISBN-13: 9781439821282
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Support Vector Machines and Their Application in Chemistry and BiotechnologySuurem pilt
  • Formaat: 211 pages
  • Ilmumisaeg: 19-Apr-2016
  • Kirjastus: CRC Press Inc
  • ISBN-13: 9781439821282
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"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"--

"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"--

Provided by publisher.
Preface vii
Author Biographies ix
Chapter 1 Overview of support vector machines
1(14)
1.1 Introduction
1(1)
1.2 Background
2(6)
1.2.1 Maximal Interval Linear Classifier
3(2)
1.2.2 Kernel Functions and Kernel Matrix
5(2)
1.2.3 Optimization Theory
7(1)
1.3 Elements of Support Vector Machines
8(2)
1.4 Applications of Support Vector Machines
10(5)
References
12(3)
Chapter 2 Support vector machines for classification and regression
15(34)
2.1 Introduction
15(1)
2.2 Kernel Functions and Dimension Superiority
16(2)
2.2.1 Notion of Kernel Functions
16(2)
2.2.2 Kernel Matrix
18(1)
2.3 Support Vector Machines for Classification
18(9)
2.3.1 Computing SVMs for Linearly Separable Case
20(2)
2.3.2 Computing SVMs for Linearly Inseparable Case
22(1)
2.3.2.1 Slack Variable-Based "Soft Margin" Technique
23(1)
2.3.2.2 Kernel Function-Based Nonlinear Mapping
23(2)
2.3.3 Application of SVC to Simulated Data
25(2)
2.4 Support Vector Machines for Regression
27(8)
2.4.1 ε-Band and ε-Insensitive Loss Function
27(1)
2.4.2 Linear ε-SVR
28(2)
2.4.3 Kernel-Based ε-SVR
30(1)
2.4.4 Application of SVR to Simulated Data
30(5)
2.5 Parametric Optimization for Support Vector Machines
35(4)
2.6 Variable Selection for Support Vector Machines
39(1)
2.7 Related Materials and Comments
39(10)
2.7.1 VC Dimension
40(1)
2.7.2 Kernel Functions and Quadratic Programming
41(1)
2.7.3 Dimension Increasing versus Dimension Reducing
41(2)
Appendix A Computation of Slack Variable-Based SVMs
43(1)
Appendix B Computation of Linear ε-SVR
44(1)
References
45(4)
Chapter 3 Kernel methods
49(28)
3.1 Introduction
49(2)
3.2 Kernel Methods: Three Key Ingredients
51(10)
3.2.1 Primal and Dual Forms
51(3)
3.2.2 Nonlinear Mapping
54(3)
3.2.3 Kernel Function and Kernel Matrix
57(4)
3.3 Modularity of Kernel Methods
61(1)
3.4 Kernel Principal Component Analysis
62(3)
3.5 Kernel Partial Least Squares
65(2)
3.6 Kernel Fisher Discriminant Analysis
67(1)
3.7 Relationship between Kernel Function and SVMs
68(4)
3.8 Kernel Matrix Pretreatment
72(1)
3.9 Internet Resources
73(4)
References
74(3)
Chapter 4 Ensemble learning of support vector machines
77(18)
4.1 Introduction
77(1)
4.2 Ensemble Learning
78(2)
4.2.1 Idea of Ensemble Learning
78(1)
4.2.2 Diversity of Ensemble Learning
79(1)
4.3 Bagging Support Vector Machines
80(1)
4.4 Boosting Support Vector Machines
81(14)
4.4.1 Boosting: A Simple Example
81(2)
4.4.2 Boosting SVMs for Classification
83(3)
4.4.3 Boosting SVMs for Regression
86(2)
4.4.4 Further Consideration
88(3)
References
91(4)
Chapter 5 Support vector machines applied to near-infrared spectroscopy
95(20)
5.1 Introduction
95(1)
5.2 Near-Infrared Spectroscopy
96(2)
5.3 Support Vector Machines for Classification of Near-Infrared Data
98(7)
5.3.1 Recognition of Blended Vinegar Based on Near-Infrared Spectroscopy
98(6)
5.3.2 Related Work on Support Vector Classification on NIR
104(1)
5.4 Support Vector Machines for Quantitative Analysis of Near-Infrared Data
105(4)
5.4.1 Correlating Diesel Boiling Points with NIR Spectra Using SVR
105(3)
5.4.2 Related Work on Support Vector Regression on NIR
108(1)
5.5 Some Comments
109(6)
References
111(4)
Chapter 6 Support vector machines and QSAR/QSPR
115(34)
6.1 Introduction
115(1)
6.2 Quantitative Structure-Activity/Property Relationship
116(4)
6.2.1 History of QSAR/QSPR and Molecular Descriptors
116(3)
6.2.2 Principles for QSAR Modeling
119(1)
6.3 Related QSAR/QSPR Studies Using SVMs
120(1)
6.4 Support Vector Machines for Regression
121(18)
6.4.1 Dataset Description
121(1)
6.4.2 Molecular Modeling and Descriptor Calculation
122(1)
6.4.3 Feature Selection Using a Generalized Cross-Validation Program
122(3)
6.4.4 Model Internal Validation
125(1)
6.4.5 PLS Regression Model
126(1)
6.4.6 BPN Regression Model
127(1)
6.4.7 SVR Model
127(8)
6.4.8 Applicability Domain and External Validation
135(3)
6.4.9 Model Interpretation
138(1)
6.5 Support Vector Machines for Classification
139(10)
6.5.1 Two-Step Algorithm: KPCA Plus LSVM
140(1)
6.5.2 Dataset Description
141(1)
6.5.3 Performance Evaluation
142(1)
6.5.4 Effects of Model Parameters
142(1)
6.5.5 Prediction Results for Three SAR Datasets
143(1)
References
144(5)
Chapter 7 Support vector machines applied to traditional Chinese medicine
149(24)
7.1 Introduction
149(1)
7.2 Traditional Chinese Medicines and Their Quality Control
149(5)
7.3 Recognition of Authentic PCR and PCRV Using SVM
154(15)
7.3.1 Background
154(1)
7.3.2 Data Description
155(1)
7.3.3 Recognition of Authentic PCR and PCRV Using Whole Chromatography
155(6)
7.3.4 Variable Selection Improves Performance of SVM
161(8)
7.4 Some Remarks
169(4)
References
169(4)
Chapter 8 Support vector machines applied to OMICS study
173(22)
8.1 Introduction
173(1)
8.2 A Brief Description of OMICS Study
173(2)
8.3 Support Vector Machines in Genomics
175(4)
8.4 Support Vector Machines for Identifying Proteotypic Peptides in Proteomics
179(9)
8.5 Biomarker Discovery in Metabolomics Using Support Vector Machines
188(1)
8.6 Some Remarks
189(6)
References
190(5)
Index 195
Yizeng Liang and Qing-Song Xu are with Central South University in Changsha, China.
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