Kernel Based Algorithms for Mining Huge Data Sets
This is the first book treating the fields of supervised, semi-supervised and unsupervised machine learning collectively. The book presents both the theory and the algorithms for mining huge data sets using support vector machines (SVMs) in an iterative way. It demonstrates how kernel based SVMs can be used for dimensionality reduction and shows the similarities and differences between the two most popular unsupervised techniques.ISDA Classification with Gaussian RBF kernel % ISDA_C.m-The main function for ISDA % % Input % n - Number of training data % x - Inputs of the ... 1); %Caching changes in O. YEm=YE; %Caching Y, B Matlab Code for ISDA Classification.
| Title | : | Kernel Based Algorithms for Mining Huge Data Sets |
| Author | : | Te-Ming Huang, Vojislav Kecman, Ivica Kopriva |
| Publisher | : | Springer - 2006-05-21 |
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