LEADER 07988nam 2200625Ia 450 001 9910782119903321 005 20230617010820.0 010 $a1-281-93460-7 010 $a9786611934606 010 $a981-279-471-9 035 $a(CKB)1000000000537777 035 $a(StDuBDS)AH24685125 035 $a(SSID)ssj0000253875 035 $a(PQKBManifestationID)11195530 035 $a(PQKBTitleCode)TC0000253875 035 $a(PQKBWorkID)10206087 035 $a(PQKB)11567464 035 $a(MiAaPQ)EBC1681735 035 $a(WSP)00005589 035 $a(Au-PeEL)EBL1681735 035 $a(CaPaEBR)ebr10255850 035 $a(CaONFJC)MIL193460 035 $a(OCoLC)815752347 035 $a(iGPub)WSPCB0005091 035 $a(EXLCZ)991000000000537777 100 $a20050213d2004 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt 182 $cc 183 $acr 200 00$aSupport vector machine in chemistry$b[electronic resource] /$fNianyi Chen ... [et al.] 210 $aSingapore ;$aHackensack, N.J. $cWorld Scientific$dc2004 215 $a1 online resource (344p.) 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a981-238-922-9 320 $aIncludes bibliographical references (p. 319-327) and index. 327 $a1. 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. 327 $a7. 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 C[symbol] 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. 330 $bIn 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. 606 $aChemistry$xData processing 606 $aChemistry, Technical$xData processing 606 $aMachine learning 615 0$aChemistry$xData processing. 615 0$aChemistry, Technical$xData processing. 615 0$aMachine learning. 676 $a540.285631 701 $aChen$b Nianyi$01531940 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910782119903321 996 $aSupport vector machine in chemistry$93777918 997 $aUNINA LEADER 01015nam0 22002771i 450 001 UON00080511 005 20231205102427.101 010 $a15-558-7673-0 100 $a20020107d1998 |0itac50 ba 101 $aeng 102 $aUS 105 $a|||| ||||| 200 1 $aWarlord politics and African states$fWilliam Reno 210 $aBoulder$cRienner$d1998 215 $axii, 257 p.$d24 cm 606 $aAfrica$xPolitica e governo$x1960-$3UONC018298$2FI 620 $aUS$dBoulder (Colorado)$3UONL000135 676 $a320.96$cPolitica e governo dell'Africa$v21 700 1$aRENO$bWilliam$3UONV046817$0661437 712 $aRienner$3UONV256908$4650 801 $aIT$bSOL$c20250620$gRICA 899 $aSIBA - SISTEMA BIBLIOTECARIO DI ATENEO$2UONSI 912 $aUON00080511 950 $aSIBA - SISTEMA BIBLIOTECARIO DI ATENEO$dSI SP 226 $eSI AA 20894 5 226 996 $aWarlord politics and African states$91300479 997 $aUNIOR