Support Vector Machine In Chemistry

Preface......Page 6
Contents......Page 8
1.1 Support Vector Machine: Data Processing Method for Problems of Small Sample Size......Page 12
1.2 Support Vector Machine: Data Processing Method for Complicated Data Sets in Chemistry......Page 14
1.3 Underfitting and Overfitting: Problems of Machine Learning......Page 17
1.4 Theory of Overfitting and Underfitting Control ERM and SRM Principles of Statistical Learning Theory......Page 23
1.5 Concept of Large Margin—A Basic Concept of SVM......Page 26
1.6 Kernel Functions: Technique for Nonlinear Data Processing by Linear Algorithm......Page 27
1.7 Support Vector Regression: Regression Based on Principle of Statistical Learning Theory......Page 30
1.9 Some Comments on the Application of SVM in Chemistry......Page 32
2.1 Margin and Optimal Separating Plane......Page 35
2.2 Interpretation by Statistical Learning Therory......Page 43
2.3 Support Vector Classification......Page 45
2.4 Support Vector Regression......Page 55
2.5 V-SVM......Page 62
3.2 Mercer Kernel......Page 64
3.3 Properties of Kernel......Page 68
3.4 Kernel Selection......Page 69
4.1 Significance and Difficulty of Feature Selection in Chemical Data Processing......Page 71
4.2 SVM-BFS — Application of Wrapper Method and Floating Search Method......Page 73
4.3 SVM-RFE: Application of Optimal Brain Damage and Recursive Feature Elimination......Page 76
4.4 Multitask Learning......Page 78
4.5 Computer Experiments: Feature Selection of Artificially Generated Data Set......Page 79
5.1 Two Different Strategies for Structure-Property Relationship Investigation......Page 85
5.2 Number of Valence Electrons of Atoms......Page 87
5.3 Ionization Potential of Atoms......Page 88
5.4 Atomic Radii and Ionic Radii......Page 90
5.5 Electronegativity......Page 101
5.6 Charge-Radius Ratio......Page 104
5.7 Topological Parameters of Molecules and 3-D Molecular Descriptors......Page 108
5.8 Atomic Parameters for Ionic Systems......Page 113
5.9 Atomic Parameters for Covalent Compounds......Page 114
5.10 Atomic Parameters for Metallic Systems......Page 115
6.1 Comprehensive Assessment and Computerized Prediction of Phase Diagrams......Page 117
6.3 Prediction of Intermediate Compound Formation......Page 120
6.4 Prediction of Formation of Extended Solid Solutions......Page 133
6.5 Prediction of Melting Types of Intermediate Compounds......Page 141
6.6 Modeling of Melting Points or Decomposition Temperature of Intermediate Compounds......Page 145
6.7 Prediction of Crystal Types of Intermediate Compounds......Page 149
6.8 Modeling of Liquid-Liquid Immiscibility of Inorganic Systems......Page 150
6.9 SVM Applied to Intelligent Database of Phase Diagrams......Page 154
7.1 Significance of Estimation of Thermodynamic Properties of Chemical Substances......Page 156
7.2 Modeling of Enthalpy of Formation of Compounds......Page 157
7.3 Modeling of Free Energy of Mixing of Liquid Alloy Systems......Page 162
7.4 Prediction of Activity Coefficient of Concentrated Electrolyte Solutions......Page 163
7.5 Regularity of the Solubility of C60 in Organic Solvents......Page 164
8.1 Concepts of Molecular Design and Materials Design......Page 167
8.2 SVM Applied to New Compound Synthesis Problems......Page 168
8.3 SVM Applied to the Computerized Prediction of Properties of Materials......Page 179
8.4 SVM Applied to Process Design for Materials Preparation......Page 187
9.1 Concept of Structure-Activity Relationships (SAR)......Page 197
9.2 Brief Introduction to Some of Chemometric Methods Used in SAR......Page 201
9.3 Brief Introduction to Molecular Descriptors Used in SAR......Page 209
9.4 SAR of N-(3-Oxo-3 4-dihydro-2H-benzo[1,4]oxazine-6-carbonyl) Guanidines......Page 212
9.5 SAR of Triazole-Derivatives......Page 216
9.6 SAR of the 5-hydroxytryptamine Receptor Antagonists......Page 222
9.7 QSAR of N-phenylacetamides as Herbicides......Page 226
10.1 Trace Element Science and Chemical Data Processing......Page 231
10.2 SVM Applied to Trace Element Analysis of Human Hair......Page 232
10.3 SVM Applied to Trace Elements Analysis of Cigarettes......Page 234
10.4 SVM Applied to Trace Element Analysis of Tea......Page 237
11.1 SVM Applied to Archeological Data Processing......Page 240
11.2 Identification of Jun Wares of Song Dynasty......Page 241
11.3 Modeling of Official Ru Wares......Page 246
11.4 Modeling of Composition of Yue Wares......Page 250
11.5 Modeling of Composition of Blue and White Porcelain Samples......Page 252
11.6 Archeological Research of Ancient Porcelain Kilns......Page 255
11.7 Period Discrimination of Ancient Samples......Page 257
12.1 SVM Applied to Cancer Epidemiology......Page 259
12.2 Carcinogenic and Environmental Behaviors of Polycyclic Aromatic Hydrocarbons......Page 266
12.3 SVM Applied to Cancer Diagnosis......Page 271
13.1 Multivariate Calibration in Chemical Analysis......Page 272
13.2 Retention Indices Estimation in Chromatography......Page 275
13.3 Detection of Hidden Explosives......Page 278
14.1 Physico-Chemical Basis of Modeling of Chemical Processes......Page 282
14.2 Characteristics of Data Processing for Industrial Process Modeling......Page 283
14.3 Optimal Zone: Strategy of Large Margin Search......Page 289
14.4 Application of Strategy of Large Margin Search......Page 291
14.5 Optimal Control for Target Maximization or Minimization......Page 295
14.6 Optimal Control for Problem of Restricted Response......Page 300
14.8 Comprehensive Strategy for Industrial Optimization......Page 310
Appendix A The Implementation of SVM......Page 316
Bibliography......Page 330
Index......Page 340