Contents -- Preface -- 1. Introduction -- 1.1 Support Vector Machine: Data Processing Method for Problems of Small Sample Size -- 1.2 Support Vector Machine: Data Processing Method for Complicated Data Sets in Chemistry -- 1.3 Underfitting and Overfitting: Problems of Machine Learning -- 1.4 Theory of Overfitting and Underfitting Control ERM and SRM Principles of Statistical Learning Theory -- 1.5 Concept of Large Margin-A Basic Concept of SVM -- 1.6 Kernel Functions: Technique for Nonlinear Data Processing by Linear Algorithm -- 1.7 Support Vector Regression: Regression Based on Principle of Statistical Learning Theory -- 1.8 Other Machine Learning Methods Related to Statistical Learning Theory -- 1.9 Some Comments on the Application of SVM in Chemistry -- 2. Support Vector Machine -- 2.1 Margin and Optimal Separating Plane -- 2.2 Interpretation by Statistical Learning Therory -- 2.3 Support Vector Classification -- 2.4 Support Vector Regression -- 2.5 V-SVM -- 3. Kernel Functions -- 3.1 Introduction -- 3.2 Mercer Kernel -- 3.3 Properties of Kernel -- 3.4 Kernel Selection -- 4. Feature Selection Using Support Vector Machine -- 4.1 Significance and Difficulty of Feature Selection in Chemical Data Processing -- 4.2 SVM-BFS - Application of Wrapper Method and Floating Search Method -- 4.3 SVM-RFE: Application of Optimal Brain Damage and Recursive Feature Elimination -- 4.4 Multitask Learning -- 4.5 Computer Experiments: Feature Selection of Artificially Generated Data Set -- 5. Principle of Atomic or Molecular Parameter-Data Processing Method -- 5.1 Two Different Strategies for Structure-Property Relationship Investigation -- 5.2 Number of Valence Electrons of Atoms -- 5.3 Ionization Potential of Atoms -- 5.4 Atomic Radii and Ionic Radii -- 5.5 Electronegativity -- 5.6 Charge-Radius Ratio.

5.7 Topological Parameters of Molecules and 3-D Molecular Descriptors -- 5.8 Atomic Parameters for Ionic Systems -- 5.9 Atomic Parameters for Covalent Compounds -- 5.10 Atomic Parameters for Metallic Systems -- 6. SVM Applied to Phase Diagram Assessment and Prediction -- 6.1 Comprehensive Assessment and Computerized Prediction of Phase Diagrams -- 6.2 Atomic Parameter-Pattern Recognition Method for Phase Diagram Prediction -- 6.3 Prediction of Intermediate Compound Formation -- 6.4 Prediction of Formation of Extended Solid Solutions -- 6.5 Prediction of Melting Types of Intermediate Compounds -- 6.6 Modeling of Melting Points or Decomposition Temperature of Intermediate Compounds -- 6.7 Prediction of Crystal Types of Intermediate Compounds -- 6.8 Modeling of Liquid-Liquid Immiscibility of Inorganic Systems -- 6.9 SVM Applied to Intelligent Database of Phase Diagrams -- 7. SVM Applied to Thermodynamic Property Prediction -- 7.1 Significance of Estimation of Thermodynamic Properties of Chemical Substances -- 7.2 Modeling of Enthalpy of Formation of Compounds -- 7.3 Modeling of Free Energy of Mixing of Liquid Alloy Systems -- 7.4 Prediction of Activity Coefficient of Concentrated Electrolyte Solutions -- 7.5 Regularity of the Solubility of C60 in Organic Solvents -- 8. SVM Applied to Molecular and Materials Design -- 8.1 Concepts of Molecular Design and Materials Design -- 8.2 SVM Applied to New Compound Synthesis Problems -- 8.3 SVM Applied to the Computerized Prediction of Properties of Materials -- 8.4 SVM Applied to Process Design for Materials Preparation -- 9. SVM Applied to Structure-Activity Relationships -- 9.1 Concept of Structure-Activity Relationships (SAR) -- 9.2 Brief Introduction to Some of Chemometric Methods Used in SAR.

9.3 Brief Introduction to Molecular Descriptors Used in SAR -- 9.4 SAR of N-(3-Oxo-3 4-dihydro-2H-benzo[l 4]oxazine-6-carbonyl) Guanidines -- 9.5 SAR of Triazole-Derivatives -- 9.6 SAR of the 5-hydroxytryptamine Receptor Antagonists -- 9.7 QSAR of N-phenylacetamides as Herbicides -- 10. SVM Applied to Data of Trace Element Analysis -- 10.1 Trace Element Science and Chemical Data Processing -- 10.2 SVM Applied to Trace Element Analysis of Human Hair -- 10.3 SVM Applied to Trace Elements Analysis of Cigarettes -- 10.4 SVM Applied to Trace Element Analysis of Tea -- 11. SVM Applied to Archeological Chemistry of Ancient Ceramics -- 11.1 SVM Applied to Archeological Data Processing -- 11.2 Identification of Jun Wares of Song Dynasty -- 11.3 Modeling of Official Ru Wares -- 11.4 Modeling of Composition of Yue Wares -- 11.5 Modeling of Composition of Blue and White Porcelain Samples -- 11.6 Archeological Research of Ancient Porcelain Kilns -- 11.7 Period Discrimination of Ancient Samples -- 12. SVM Applied to Cancer Research -- 12.1 SVM Applied to Cancer Epidemiology -- 12.2 Carcinogenic and Environmental Behaviors of Polycyclic Aromatic Hydrocarbons -- 12.3 SVM Applied to Cancer Diagnosis -- 13. SVM Applied to Some Topics of Chemical Analysis -- 13.1 Multivariate Calibration in Chemical Analysis -- 13.2 Retention Indices Estimation in Chromatography -- 13.3 Detection of Hidden Explosives -- 14. SVM Applied to Chemical and Metallurgical Technology -- 14.1 Physico-Chemical Basis of Modeling of Chemical Processes -- 14.2 Characteristics of Data Processing for Industrial Process Modeling -- 14.3 Optimal Zone: Strategy of Large Margin Search -- 14.4 Application of Strategy of Large Margin Search -- 14.5 Optimal Control for Target Maximization or Minimization.

14.6 Optimal Control for Problem of Restricted Response -- 14.7 Materials Properties Estimation for Production Process -- 14.8 Comprehensive Strategy for Industrial Optimization -- Appendix A The Implementation of SVM -- Bibliography -- Index.