| Titre : | Support Vector Machines Optimization Based Theory, Algorithms, and Extensions |
| Auteurs : | Naiyang Deng, Auteur ; Yingjie Tian, Auteur ; Chunhua Zhang, Auteur |
| Type de document : | Monographie imprimée |
| Editeur : | CRC Press, 2013 |
| ISBN/ISSN/EAN : | 978-1-4398-5792-2 |
| Format : | 1vol(P364) / 24cm |
| Langues: | Anglais |
| Mots-clés: | Machines.Algoritms |
| Résumé : |
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. |
| Sommaire : |
Optimization Optimization Problems in Euclidian Space Convex Programming in Euclidean Space Convex Programming in Hilbert Space Convex Programming with Generalized Inequality Constraints in Rn Convex Programming with Generalized Inequality Constraints in Hilbert Space Linear Classification Machines Presentation of Classification Problems Support Vector Classification (SVC) for Linearly Separable Problems Linear Support Vector Classification Linear Regression Machines Regression Problems and Linear Regression Problems Hard ε-Band Hyperplane Linear Hard ε-Band Support Vector Regression Linear ε-Support Vector Regression Kernels and Support Vector Machines From Linear Classification to Nonlinear Classification Kernels Support Vector Machines and Their Properties Meaning of Kernels Basic Statistical Learning Theory of C-Support Vector Classification Classification Problems on Statistical Learning Theory Empirical Risk Minimization Vapnik Chervonenkis (VC) Dimension Structural Risk Minimization An Implementation of Structural Risk Minimization Theoretical Foundation of C-Support Vector Classification on Statistical Learning Theory Model Construction Data Generation Data Preprocessing Model Selection Rule Extraction Implementation Stopping Criterion Chunking Decomposing Sequential Minimal Optimization Software Variants and Extensions of Support Vector Machines Variants of Binary Classification Variants of Regression Multi-Class Classification Semi-Supervised Classification Universum Classification Privileged Classification Knowledge-Based Classification Robust Classification Multi-Instance Classification Multi-Label Classification Bibliography Index Author(s) |
Disponibilité (2)
| Cote | Support | Localisation | Statut |
|---|---|---|---|
| INF/796 | Livre | bibliothèque sciences exactes | Consultable |
| INF/796 | Livre | bibliothèque sciences exactes | Empruntable |
