Description:

MLPACK is the first comprehensive scalable machine learning library. Developed by the Fundamental Algorithmic and Statistical Tools laboratory (FASTlab), MLPACK and its core functions library FASTlib are the much needed filling of an existing void. Previously, researchers had to either (a) settle for poorly-scaling collections of methods implemented for academic purposes, (b) hunt down the often difficult to find and difficult to apply yet fast code writen by algorithms' developers, or (c) reimplement solutions to their specific analysis problems from scratch. With MLPACK, we offer a fourth option, in which researchers may find all the methods they need designed favoring both speed and usability.

MLPACK currently includes a wide range of the following efficient algorithms:

• $k$-nearest neighbor classifier.

• FastICA.

• Hidden Markov Models.

• Information Maximization algorithm for ICA.

• Kalman filter.

• Kernel density estimation algorithm using series expansion.

• Mixture of Gaussians using maximum likelihood and L2 error.

• Naive Bayes classifier.

• Nelder-Mead/Quasi-Newton optimizer.

• Series expansion library for Gaussian kernel in $O(p^D)$ and $O(D^p)$ expansions.

• Support Vector Machine classifier and regression.

• Sequential Minimal Optimization algorithm for SVM.

BibTeX Entry:

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Corresponding Paper BibTeX Entry:

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Supported Operating Systems:

Cygwin, Linux, Macosx

Tags:

Clustering, Kernel Methods, Convex Optimization, Classifiaction, Density Estimation, Large Scale Learning, Kalman Filter, K Nearest Neighbor Classification, Algorithms, Classifiers, Nips2008, Kdtree

Archive:

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