Support Vector Machines Optimization Based Theory, Algorithms, and Extensions Chapman & Hall/CRC Data Mining and Knowledge Discovery Series
Auteurs : Deng Naiyang, Tian Yingjie, Zhang Chunhua
Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions presents an accessible treatment of the two main components of support vector machines (SVMs)?classification problems and regression problems. The book emphasizes the close connection between optimization theory and SVMs since optimization is one of the pillars on which SVMs are built.
The authors share insight on many of their research achievements. They give a precise interpretation of statistical leaning theory for C-support vector classification. They also discuss regularized twin SVMs for binary classification problems, SVMs for solving multi-classification problems based on ordinal regression, SVMs for semi-supervised problems, and SVMs for problems with perturbations.
To improve readability, concepts, methods, and results are introduced graphically and with clear explanations. For important concepts and algorithms, such as the Crammer-Singer SVM for multi-class classification problems, the text provides geometric interpretations that are not depicted in current literature.
Enabling a sound understanding of SVMs, this book gives beginners as well as more experienced researchers and engineers the tools to solve real-world problems using SVMs.
Optimization. Linear Classification Machines. Linear Regression Machines. Kernels and Support Vector Machines. Basic Statistical Learning Theory of C-Support Vector Classification. Model Construction. Implementation. Variants and Extensions of Support Vector Machines. Bibliography. Index.
Date de parution : 12-2012
Ouvrage de 300 p.
15.6x23.4 cm
Thèmes de Support Vector Machines :
Mots-clés :
Support Vector Classification; Final Decision Function; SVMs for classification and regression problems; Convex Programming Problem; optimization theory and support vector machines; Vector Classification; statistical leaning theory for C-support vector classification; Support Vector Regression; regularized twin SVMs for binary classification problems; Decision Function; SVMs for multi-classification problems; Training Points; SVMs for semi-supervised problems; Support Vector Machines; SVMs for problems with perturbations; Convex Quadratic Programming Problem; Crammer-Singer SVM; Semidefinite Programming; solve real-world problems using SVMs; Convex Quadratic Programming; Universum classification problem; Exist Lagrange Multipliers; Dual Problem; Feasible Point; Training Set; Linearly Separable; Positive Semidefinite; Loo Error; Binary Classification Problem; Initial Training Set; SRM; Convex Programming; Roc; VC Dimension; SRM Principle