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.
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.
Codes for ISDA
The software SemiL is the first program that implements graph-based semi-supervised learning techniques for large-scale problems.
PCA & ICA Algorithms: MATLAB codes for unsupervised
learning PCA & ICA algorithms for the examples in the book.