Support vector machine

Support Vector Machines
Tutorial Slides by Andrew Moore
We review the idea of the margin of a classifier, and why that may be a good criterion for measuring a classifier's desirability.

Support Vector Machines (SVM) Introductory Overview
Support Vector Machines are based on the concept of decision planes that define decision boundaries.

1 From support vector machine to least squares support vector machine
1.1 Inseparable data
1.2 Least squares SVM formulation
1.3 Kernel function K ...

Support Vector Machines
Machine Learning domain NN approaches are considered to be a baseline technique for data-driven modeling. The advanced Machine Learning techniques include Support Vector Machines.

Support Vector Machine A generalized linear classifier with a maximum-margin fitting criterion. This fitting criterion provides regularization which helps the classifier generalize better. The classifier tends to ignore many of the features.

Support vector machine
A support vector machine (SVM) is a recently developed form of machine learning algorithm. The training of SVMs is based on quadratic programming, a form of optimization that (usually) has only one global minimum.

Somehow, support vector machines learned the characteristic features of functional change in neural connectivity between the ages of 7 and 30 years. How exactly did they manage this?
Read on » ...

Chapter 6 Support Vector Machines 268
6.1 Introduction 268
6.2 Optimal Hyperplane for Linearly Separable Patterns 269
6.3 Optimal Hyperplane for Nonseparable Patterns 276
6.4 The Support Vector Machine Viewed as a Kernel Machine 281
6.

The most widely used classifiers are the neural network,[106] kernel methods such as the support vector machine,[107] k-nearest neighbor algorithm,[108] Gaussian mixture model,[109] naive Bayes classifier,[110] and decision tree.

Hierarchical K-means Clustering Using New Support Vector Machines for Multi-class Classification,International Joint Conference on Neural Networks, 2006.

These include classification algorithms such as decision trees, neural nets, Bayesian classifiers, Support Vector Machines and cased-based (aka non-parametric) learning.

These advantages are, however, bought at a price: many powerful optimization techniques (such as: conjugate and second-order gradient methods, support vector machines, Bayesian methods, etc.) - which we will not talk about in this course! ...

See also: Classification, Neural network, Regression, Data mining, Distribution