This toolbox (MLT, or Machine Learning Toolbox) provides a number of essential functions for machine learning, especially for data clustering and pattern recognition. The full version of MLT is published by the Terasoft. You can download the complimentary demo version which has same functionality for a limited time.
After downloading the toolbox, you may want to try some of the interesting demos:
gradientDescentDemo: Interactive demo of gradient descent over the peaks function
- gmmTrainDemo2dCovType01: Animation of GMM (Gaussian mixture models) training with isotropic covariance matrices
- gmmTrainDemo2dCovType02: Animation of GMM (Gaussian mixture models) training with diagonal covariance matrices
- gmmTrainDemo2dCovType03: Animation of GMM (Gaussian mixture models) training with full covariance matrices
- gmmGrowDemo: Animation of GMM (Gaussian mixture models) growing with center splitting
- hierClusteringPlot: Step-by-step hierarchical clustering
taylorExpansionDemo: Interactive demo of polynomial fit and taylor expansios lcs and editDistance: Visualize the result of dynamic programming for LCS (longest common subsequence) and ED (edit distance)
If you are using the toolbox for your research, please kindly give reference as follows:
Jyh-Shing Roger Jang, "Machine Learning Toolbox", available at "http://mirlab.org/jang/matlab/toolbox/machineLearning", accessed on [date].
If you run into the problem of "Invalid MEX-file... The specified module could not be found" under MS Windows, please install Visual C++ runtime at http://www.microsoft.com/en-us/download/details.aspx?id=30679.