Section 4.13.0 - Introductory Writeups
Section 4.13.0.1 - Basic Writeups
Section 4.13.0.1.1 - Part 1 - Wikipedia - Support Vector Machine
Section 4.13.0.1.2 - Part 2 - Savan Patel - SVM Theory
Section 4.13.0.1.3 - Part 3 - Ajay Yadav - Support Vector Machines
Section 4.13.0.2 - Detailed Writeups
Section 4.13.0.2.1 - Part 1 - Ng - Support Vector Machines
Section 4.13.0.2.2 - Part 2 - Jakkula - Tutorial on Support Vector Machine (SVM)
Section 4.13.0.2.3 - Part 3 - Ben-Hur,Weston - A User’s Guide to Support Vector Machines
Section 4.13.0.2.4 - Part 4 - Berwick - An Idiot’s guide to Support vector machines (SVMs)
Section 4.13.1 - Linear SVM
Section 4.13.1.0 - Basic Writeup - JMLR - Linear SVM
Section 4.13.1.1 - Hard-margin SVM
Section 4.13.1.1.0 - Introductory Writeups
Section 4.13.1.1.0.1 - Basic Writeup - Wikipedia - SVM: Hard Margin
Section 4.13.1.1.0.2 - Detailed Writeup - Boser,Guyon,Vapnik - A Training Algorithm for Optimal Margin Classifiers
Section 4.13.1.2 - Soft-margin SVM
Section 4.13.1.2.0 - Introductory Writeups
Section 4.13.1.2.0.1 - Basic Writeup - Wikipedia - SVM: Soft Margin
Section 4.13.1.2.0.2 - Detailed Writeup - Cortes,Vapnik - Support Vector Networks
Section 4.13.1.3 - Langrangian SVM
Section 4.13.1.3.0 - Introductory Writeups
Section 4.13.1.3.0.1 - Basic Writeup - JMLR - Lagrangian SVM
Section 4.13.1.3.0.2 - Detailed Writeup - Mangasarian,Musicant - Lagrangian Support Vector Machines
Section 4.13.2 - Non-Linear SVM
Section 4.13.2.0 - Introductory Writeups
Section 4.13.2.0.1 - Basic Writeups
Section 4.13.2.0.1.1 - Part 1 - Wikipedia - SVM: NonLinear Classification
Section 4.13.2.0.1.2 - Part 2
Section 4.13.2.0.2 - Detailed Writeup - Rai - Kernel Methods and Nonlinear Classification
Section 4.13.2.1 - Common Kernel Tricks in SVMs(Mappings from Non-Linear to Linear Space)
Section 4.13.2.1.0 - Basic Writeup - Wikipedia - SVM: Kernel Trick
Section 4.13.2.1.1 - Polynomial (homogeneous)
Section 4.13.2.1.2 - Polynomial (inhomogeneous)
Section 4.13.2.1.3 - Gaussian Radial Based Function
Section 4.13.2.1.4 - Hyperbolic Tangent
Section 4.13.2.1.5 - 25 Kernel Tricks (All on one page with code pointers)
Section 4.13.3 - Computing the SVM classifier
Section 4.13.3.0 - Basic Writeup - Wikipedia - SVM: Computing the SVM Classifier
Section 4.13.3.1 - Primal
Section 4.13.3.2 - Dual
Section 4.13.3.3 - Kernel trick
Section 4.13.3.4 - Modern methods
Section 4.13.3.4.0 - Basic Writeup - Wikipedia - SVM: Modern Methods
Section 4.13.3.4.1 - Sub-gradient descent
Section 4.13.3.4.2 - Coordinate descent
Section 4.13.3.5 - Empirical risk minimization
Section 4.13.3.5.0 - Basic Writeup - Wikipedia - Empirical Risk Minimization
Section 4.13.3.5.1 - Risk minimization (See Section 8.1.4.1 on Loss Functions for details)
Section 4.13.3.5.2 - Regularization and stability (See Section 4.18 on Regularization)
Section 4.13.3.5.3 - SVM and the hinge loss (See Section 8.1.4.1.3.2 Classification of Loss Functions)
Section 4.13.4 - Extensions
Section 4.13.4.1 - Support vector clustering (SVC)
Section 4.13.4.2 - Multiclass SVM
Section 4.13.4.2.0 - Introductory Writeups
Section 4.13.4.2.0.1 - Basic Writeup - Stanford - Multi-Class SVM
Section 4.13.4.2.0.2 - Detailed Writeups
Section 4.13.4.2.0.2.1 - Part 1 - Ahuja,Yadav - Multiclass Classification and Support Vector Machine
Section 4.13.4.2.0.2.2 - Part 2 - Hsu,Lin - A Comparison of Methods for Multi-class Support Vector Machines
Section 4.13.4.2.1 - One-vs-One Classification
Section 4.13.4.2.2 - One-vs-All Classification
Section 4.13.4.2.2.0 - Basic Writeup - Scikit - Multi-Class: One-vs-All Classification
Section 4.13.4.2.2.1 - Multi-Class Classification
Section 4.13.4.2.2.2 - Multilabel Classification
Section 4.13.4.2.3 - Directed Acyclic Graph SVM (DAGSVM)
Section 4.13.4.2.4 - Error Correcting Output Codes
Section 4.13.4.2.4.0 - Basic Writeup - Dietterich,Bakiri - Solving Multiclass Learning Problems via Error-Correcting Output Codes
Section 4.13.4.2.4.1 - Top-K
Section 4.13.4.3 - Transductive support vector machines
Section 4.13.4.4 - Structured SVM
Section 4.13.4.4.0 - Introductory Writeups
Section 4.13.4.4.0.1 - Basic Writeup - Wikipedia - Structured SVM
Section 4.13.4.4.0.2 - Detailed Writeup - Joachims,Hofmann,Yue,Yu - Predicting Structured Objects with Support Vector Machines
Section 4.13.4.5 - SVM Regression
Section 4.13.4.5.1 - Detailed Writeups
Section 4.13.4.5.1.1 - Part 1 - Smola,Sch¨olkopf - A Tutorial on Support Vector Regression
Section 4.13.4.5.1.2 - Part 2 - Drucker,Burges,Kaufman,Smola,Vapnik - Support Vector Regression Machines
Section 4.13.4.6 - Bayesian SVM
Section 4.13.4.6.1 - Detailed Writeups
Section 4.13.4.6.1.1 - Part 1 - Thesis - Wei - Bayesian Approach to Support Vector Machines
Section 4.13.4.6.1.2 - Part 2 - Sollich - Bayesian Methods for Support Vector Machines: Evidence and Predictive Class Probabilities
Section 4.13.4.7 - SVM Clustering
Section 4.13.4.7.1 - Detailed Writeups
Section 4.13.4.7.1.1 - Part 1 - Ben-Hur,Horn,Siegelmann,Vapnik - Support Vector Clustering
Section 4.13.4.7.1.2 - Part 2 - Hilt,Merat - SVM Clustering
Section 4.13.4.7.1.3 - Part 3 - Finley,Joachims - Supervised Clustering with Support Vector Machines
Section 4.13.4.8 - One-Class SVM for Outlier Detection (See Section 4.8.1.5.1 Unsupervised Anomaly Detection - Classification Based
