Main features are: - Except for the QP solver, all parts are written in plain Matlab. This guarantees for easy modification. Special kinds of kernels that require much computation (such as the Fisher kernel, which is based on a model of the data) ca
Abstract. The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data, working through a non-trivial example in de
超球结构支持向量机用于多分类问题。Support vector machines (SVM) are learning algorithms derived from statistical learning theory. The SVM approach was originally developed for binary classification problems. For solving multi-class classification problem, there are
很有用的外文资源：The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data, working through a non-trivial example in det
An overview of statistical learning theory, containing no proofs, but most of the crucial theorems and milestones of learning theory. With a detailed chapter on SVMs for pattern recognition and regression
SVMs (Support Vector Machines) are a useful technique for data classi cation. Al- though SVM is considered easier to use than Neural Networks, users not familiar with it often get unsatisfactory results at rst. Here we outline a \cookbook" approach
This is the first comprehensive introduction to SVMs, a new generation learning system based on recent advances in statistical learning theory; it will help readers understand the theory and its real-world applications.
libsvm的使用实例，很容易掌握，适用于初学者。SVMs (Support Vector Machines) are a useful technique for data classification. Although SVM is considered easier to use than Neural Networks, users not familiar with it often get unsatisfactory results at first. Here we outl
The goal of this book is to explain the principles that made support vector machines (SVMs) a successful modeling and prediction tool for a variety of applications. We try to achieve this by presenting the basic ideas of SVMs together with the lates
This is the first comprehensive introduction to SVMs, a new generation learning system based on recent advances in statistical learning theory; it will help readers understand the theory and its real-world applications.
本资料包括实验要求文档，报告文档，训练及测试数据，matlab源代码。就给定问题，利用SVM来进行分类。SVM包括hardmargin的线性和非线性内核，softmargin的线性和非线性内核分别来分类以及评估分类准确度-a MATLAB (M-file) program to compute the discriminant functiong for the following SVMs, using the training set provided：A hard-margin SVM
The machine learning toolbox's focus is on large scale kernel methods and especially on Support Vector Machines (SVM) [1]. It provides a generic SVM object interfacing to several different SVM implementations, among them the state of the art LibSVM
