BESAML: Barely Efficient and ScalAble Machine Learning toolbox
A personal Machine Learning MATLAB toolbox. Algorithms are implemented in a simple and readable way.
Part of this code is inspired by the code of Coursera's Machine Learning course by Andrew Ng. If you arrived here looking for a Deep Learning toolbox you probably were looking for Lasagne, only related to this in a gastronomical way.
The main use of this toolbox is education and research and, according to its name, I do not recomend it for data-intensive production environments. In such a case, you can contact me at email@example.com for profesional advice.
besaml_setup.m file before using the toolbox.
- Modelling of data using a Gaussian Mixture Model (GMM) fitted using Expectation-Maximization (EM).
- Multiclass Softmax Regression Classifier.
I have included some small datasets to test the implemented algorithms.
- Old Faithful Geiser Dataset [1,2].
- Subset of MNIST dataset  selected in Coursera's Machine Learning course.
fmincg. Copyright (C) 2001 and 2002 by Carl Edward Rasmussen. Date 2002-02-13.
Besaml is a work in progress, any comment would be welcomed.
 Hardle, W. (1991) Smoothing Techniques with Implementation in S. New York: Springer.  Azzalini, A. and Bowman, A. W. (1990). A look at some data on the Old Faithful geyser. Applied Statistics 39, 357-365.  Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. "Gradient-based learning applied to document recognition." Proceedings of the IEEE, 86(11):2278-2324, November 1998.