Review: Support Vector Machines in Pattern Recognition

Review: Support Vector Machines in Pattern Recognition

SVM is extensively used in pattern recognition because of its capability to classify future unseen data and its’ good generalization performance. Several algorithms and models have been proposed for pattern recognition that uses SVM for classification. These models proved the efficiency of SVM in pattern recognition. Researchers have compared their results for SVM with other traditional empiric...

Support Vector Machines and Pattern Recognition

The following is a report on my topic “Support Vector Machines and Pattern Recognition.” I basically give an outline of the general pattern recognition problem, and how Support Vector Machines can be used to solve it. Since this is an introductory class, the treatment of the topic will be less mathematical than most research papers in this area, but I hope to give an intuitive understanding of ...

A Survey on Pattern Recognition Applications of Support Vector Machines

The SVM is a new type of pattern classifier based on a novel statistical learning technique that has been recently proposed by Vapnik and his co-workers. Unlike traditional methods such as neural networks, which minimize the empirical training error, SVMs aim at minimizing an upper bound of the generalization error through maximizing the margin between the separating hyperplane and the data. Si...

A Tutorial on Support Vector Machines for Pattern Recognition

Support vector machines for multi-class pattern recognition

The solution of binary classiication problems using support vector machines SVMs is well developed, but multi-class problems with more than two classes have t ypically been solved by combining independently produced binary classiiers. We propose a formulation of the SVM that enables a multi-class pattern recognition problem to be solved in a single optimisation. We also propose a similar genera...