Supervised, Semi-supervised, and Unsupervised Learning

Kernel Based Algorithms for Mining Huge Data Sets


new LinearSVM: The newest extremely fast machine learning (data mining) algorithm for solving multiclass classification problems from ultra large data sets that implements an original proprietary version of a cutting plane algorithm for designing a linear support vector machine. LinearSVM is a linearly scalable routine meaning that it creates an SVM model in a CPU time which scales linearly with the size of the training data set.

Chapter 3

ISDA: A support vector machines tool with a nice GUI for solving large-scale classification and regression problems. 

If you are using results and analysis by the help of ISDA software in your publications please make the reference to:  Huang T.-M., V. Kecman, I. Kopriva, Kernel Based Algorithms for Mining Huge Data Sets, Supervised, Semi-supervised, and Unsupervised Learning, Springer-Verlag, Berlin, Heidelberg, 2006.


Chapter 5

SemiL: The software SemiL is the first program that implements graph-based semi-supervised learning techniques for large-scale problems.

Chapter 6

Unsupervised PCA & ICA Algorithms: MATLAB codes for unsupervised learning  PCA & ICA algorithms for the examples in the book.




Copyright Huang, Kecman and Kopriva © 2006 All Rights Reserved