Support Vector Machines and Their Application in Chemistry and Biotechnology

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Opis: Support Vector Machines and Their Application in Chemistry and Biotechnology - Qing-Song Xu, Dong-Sheng Cao, Hong-Dong Li

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 I -Band and I -Insensitive Loss Function Linear I -SVR Kernel-Based I -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 I -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


Szczegóły: Support Vector Machines and Their Application in Chemistry and Biotechnology - Qing-Song Xu, Dong-Sheng Cao, Hong-Dong Li

Tytuł: Support Vector Machines and Their Application in Chemistry and Biotechnology
Autor: Qing-Song Xu, Dong-Sheng Cao, Hong-Dong Li
Producent: CRC Press Inc.
ISBN: 9781439821275
Rok produkcji: 2011
Ilość stron: 211
Oprawa: Twarda
Waga: 0.42 kg


Recenzje: Support Vector Machines and Their Application in Chemistry and Biotechnology - Qing-Song Xu, Dong-Sheng Cao, Hong-Dong Li

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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 I -Band and I -Insensitive Loss Function Linear I -SVR Kernel-Based I -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 I -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

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Szczegóły: Support Vector Machines and Their Application in Chemistry and Biotechnology - Qing-Song Xu, Dong-Sheng Cao, Hong-Dong Li

Tytuł: Support Vector Machines and Their Application in Chemistry and Biotechnology
Autor: Qing-Song Xu, Dong-Sheng Cao, Hong-Dong Li
Producent: CRC Press Inc.
ISBN: 9781439821275
Rok produkcji: 2011
Ilość stron: 211
Oprawa: Twarda
Waga: 0.42 kg


Recenzje: Support Vector Machines and Their Application in Chemistry and Biotechnology - Qing-Song Xu, Dong-Sheng Cao, Hong-Dong Li

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