(more than 4000 downloads on mloss.org)
MLweb is an open source project that aims at bringing machine learning capabilities into web pages and web applications, while maintaining all computations on the client side. It includes:
(more than 10 000 downloads on mloss.org)
MSVMpack is an open source package dedicated to multi-class support vector machines: SVMs which can handle more than two classes without relying on decomposition methods. The aim is to provide a unified framework and implementation for all the different M-SVM models in a single package.The package provides a set of command-line tools for training and testing M-SVMs together with a C API.
MSVMpack is available for Linux and Mac OS X (uses POSIX threads for parallelization).
SparsePoly is a Matlab implementation of the polynomial basis pursuit and greedy algorithms described in
F. Lauer and H. Ohlsson, Finding sparse solutions of polynomial systems of equations via group sparsity optimization, Journal of Global Optimization, 62(2):319-349, 2015.
It includes L1 and L1/L2 basis pursuit methods with two different iteratively reweighting schemes, and both approximate and exact greedy algorithms for solving the group sparse optimization problems.
The toolbox uses MOSEK to solve the convex optimization programs.
k-LinReg is an open source software dedicated to switched linear regression with large data sets. It is based on a simple k-means like algorithm which provides both speed and accuracy.
Applications of k-LinReg include for instance hybrid (switched linear or piecewe affine) dynamical system identification.
k-LinReg is available as a platform-independent Matlab implementation or a parallel implementation in C for Linux.
COFSR is a Matlab implementation of the continuous optimization framework described in
F. Lauer, G. Bloch, and R. Vidal, A continuous optimization framework for hybrid system identification, Automatica, 47(3):608-613, 2011,
for switched regression and hybrid system identification.
Compared to k-LinReg, it offers multiple choices for the form of the objective function (smooth product-of-error type or nonsmooth minimum-of-error type), the regularization and the loss function, including losses that are robust to outliers.
The package also includes the code by Huyer and Neumaier for the MCS algorithm.