1st Edition
Support Vector Machines and Their Application in Chemistry and Biotechnology
Support vector machines (SVMs) are used in a range of applications, including drug design, food quality control, metabolic fingerprint analysis, and microarray data-based cancer classification. While most mathematicians are well-versed in the distinctive features and empirical performance of SVMs, many chemists and biologists are not as familiar with what they are and how they work. Presenting a clear bridge between theory and application, Support Vector Machines and Their Application in Chemistry and Biotechnology provides a thorough description of the mechanism of SVMs from the point of view of chemists and biologists, enabling them to solve difficult problems with the help of these powerful tools.
Topics discussed include:
- Background and key elements of support vector machines and applications in chemistry and biotechnology
- Elements and algorithms of support vector classification (SVC) and support vector regression (SVR) machines, along with discussion of simulated datasets
- The kernel function for solving nonlinear problems by using a simple linear transformation method
- Ensemble learning of support vector machines
- Applications of support vector machines to near-infrared data
- Support vector machines and quantitative structure-activity/property relationship (QSAR/QSPR)
- Quality control of traditional Chinese medicine by means of the chromatography fingerprint technique
- The use of support vector machines in exploring the biological data produced in OMICS study
Beneficial for chemical data analysis and the modeling of complex physic-chemical and biological systems, support vector machines show promise in a myriad of areas. This book enables non-mathematicians to understand the potential of SVMs and utilize them in a host of applications.
Overview of support vector machines
Background
Maximal Interval Linear Classifier
Kernel Functions and Kernel Matrix
Optimization Theory
Elements of Support Vector Machines
Applications of Support Vector Machines
Support vector machines for classification and regression
Kernel Functions and Dimension Superiority
Notion of Kernel Functions
Kernel Matrix
Support Vector Machines for Classification
Computing SVMs for Linearly Separable Case
Computing SVMs for Linearly Inseparable Case
Application of SVC to Simulated Data
Support Vector Machines for Regression
ε-Band and ε-Insensitive Loss Function
Linear ε-SVR
Kernel-Based ε-SVR
Application of SVR to Simulated Data
Parametric Optimization for Support Vector Machines
Variable Selection for Support Vector Machines
Related Materials and Comments
VC Dimension
Kernel Functions and Quadratic Programming
Dimension Increasing versus Dimension Reducing
Appendix A: Computation of Slack Variable-Based SVMs
Appendix B: Computation of Linear ε-SVR
Kernel methods
Kernel Methods: Three Key Ingredients
Primal and Dual Forms
Nonlinear Mapping
Kernel Function and Kernel Matrix
Modularity of Kernel Methods
Kernel Principal Component Analysis
Kernel Partial Least Squares
Kernel Fisher Discriminant Analysis
Relationship between Kernel Function and SVMs
Kernel Matrix Pretreatment
Internet Resources
Ensemble learning of support vector machines
Ensemble Learning
Idea of Ensemble Learning
Diversity of Ensemble Learning
Bagging Support Vector Machines
Boosting Support Vector Machines
Boosting: A Simple Example
Boosting SVMs for Classification
Boosting SVMs for Regression
Further Consideration
Support vector machines applied to near-infrared spectroscopy
Near-Infrared Spectroscopy
Support Vector Machines for Classification of
Near-Infrared Data
Recognition of Blended Vinegar Based on
Near-Infrared Spectroscopy
Related Work on Support Vector Classification on NIR
Support Vector Machines for Quantitative Analysis of
Near-Infrared Data
Correlating Diesel Boiling Points with NIR Spectra
Using SVR
Related Work on Support Vector Regression on NIR
Some Comments
Support vector machines and QSAR/QSPR
Quantitative Structure-Activity/Property Relationship
History of QSAR/QSPR and Molecular Descriptors
Principles for QSAR Modeling
Related QSAR/QSPR Studies Using SVMs
Support Vector Machines for Regression
Dataset Description
Molecular Modeling and Descriptor Calculation
Feature Selection Using a Generalized
Cross-Validation Program
Model Internal Validation
PLS Regression Model
BPN Regression Model
SVR Model
Applicability Domain and External Validation
Model Interpretation
Support Vector Machines for Classification
Two-Step Algorithm: KPCA Plus LSVM
Dataset Description
Performance Evaluation
Effects of Model Parameters
Prediction Results for Three SAR Datasets
Support vector machines applied to traditional Chinese medicine
Introduction
Traditional Chinese Medicines and Their Quality Control
Recognition of Authentic PCR and PCRV Using SVM
Background
Data Description
Recognition of Authentic PCR and PCRV Using
Whole Chromatography Variable Selection Improves Performance of SVM
Some Remarks
Support vector machines applied to OMICS study
A Brief Description of OMICS Study
Support Vector Machines in Genomics
Support Vector Machines for Identifying Proteotypic
Peptides in Proteomics
Biomarker Discovery in Metabolomics Using Support
Vector Machines
Some Remarks
Index
Biography
Yizeng Liang and Qing-Song Xu are with Central South University in Changsha, China.
