Projects / MLPACK


MLPACK is a C++ machine learning library with an emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and maximum flexibility for expert users. It contains algorithms such as k-means, Gaussian mixture models, hidden Markov models, density estimation trees, kernel PCA, locality-sensitive hashing, sparse coding, linear regression and least-angle regression.

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Recent releases

  •  07 Jan 2014 05:03

    Release Notes: This release adds logistic regression and expands GMM training by allowing the use of an existing model as a starting point. A memory leak in NeighborSearch has been fixed.

    •  08 Oct 2013 01:29

      Release Notes: This release added a collaborative filtering package that can provide recommendations when given users and items. It also contains speedups for Kernel PCA and various bugfixes.

      •  25 Aug 2013 02:55

        Release Notes: This release has added rank-approximate nearest neighbors, fast exact max-kernel search, and more parameters for the Baum-Welch algorithm used for training hidden Markov models. It has various fixes, including a fix for EM covariance estimation that reduces training time of Gaussian mixture models.


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