This toolbox (MLT, or Machine Learning Toolbox) provides a number of essential functions for machine learning. You can download the toolbox here.
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.