svm application example

1.LIBSVM and Liblinear differences, simple source analysis. http://blog.csdn.net/zhzhl202/article/details/7438160 http://blog.csdn.net/zhzhl202/article/details/7438313LIBSVM is a software that integrates support vector machines (c-svc, nu-svc),

The rapid development and improvement of SVM shows many unique advantages in solving small-sample, nonlinear and high-dimensional pattern recognition problems, and can be applied to other machine learning problems such as function fitting. From this

Original: http://blog.csdn.net/suipingsp/article/details/41645779Support Vector machines are basically the best supervised learning algorithms, because their English name is SVM. In layman's terms, it is a two-class classification model, whose basic

Support Vector machines are basically the best supervised learning algorithms, because their English name is SVM. In layman's terms, it is a two-class classification model, whose basic model is defined as the most spaced linear classifier on the

Original: http://blog.csdn.net/arthur503/article/details/19966891Before thinking that SVM is very powerful and mysterious, I understand the principle is not difficult, but, "the master's skill is to use the idea of mathematics to define it, using

The theory knowledge of SVM see some summarization and cognition of SVM--entry level Before always thought, using SVM to do the classification, is not to use multiple SVM classification, please shape similar to a binary tree, as follows: That is,

Part 1 Introduction   Data-based machine learning is an important aspect of modern intelligent technology. It studies the laws from the perspective of observation data (samples) and uses these rules to predict future data or unobserved data.

I have worked on some text mining projects, such as Webpage Classification, microblog sentiment analysis, and user comment mining. I also packaged libsvm and wrote the text classification software tmsvm. So here we will summarize some of the

SVM is widely used in classification, regression, density estimation, clustering, etc. But I think the most successful is classification. When used for classification problems, there are not many parameters available for SVM. The penalty parameter C,

IntroductionSVM (Support vector MACHINE,SVM) is the maximal interval linear classifier defined in the feature space, and in the case of nonlinear data, the kernel method (kernel trick) is used to make it become a nonlinear classifier in essence.