This is a repository of databases, domain theories and data generators that are used by the machine learning community for the empirical analysis of machine learning algorithms.
In the previous post on Support Vector Machines (SVM), we looked at the mathematical details of the algorithm. In this post, I will be discussing the practical implementations of SVM for classification as well as regression. I will be using the iris dataset as an example for the classification problem, and a randomly generated data as an example for the regression problem.
A collection of command-line tools for researchers in machine learning, data mining, and related fields. All of the functionality is also provided in a clean C++ class library
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Y. Zhou, L. Wu, F. Weng, and H. Schmidt. Proceedings of the 2003 conference on Empirical methods in natural language processing, page 153--159. Morristown, NJ, USA, Association for Computational Linguistics, (2003)