MLPACK (C++ library)

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Type Private
Industry Machine Learning
Founded 2011
Headquarters Atlanta, Georgia
Key people [1]
Investors Fast Track at Georgia Tech.
Related Certifications Certificate in Machine Learning Industry Overview

mlpack is a C++ Machine Learning library with 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. This is done by providing a set of command-line executables which can be used as black boxes, and a modular C++ API for expert users and researchers to easily make changes to the internals of the algorithms.[2]
mlpack is supported by Georgia Institute of Technology and contributions from around the world. around the world. It is released free of charge, under the 3-clause BSD License (more information). (Versions older than 1.0.12 were released under the GNU Lesser General Public License: LGPL, version 3.)


  • Originally, mlpack was produced by the FASTLab[3] at Georgia Tech[4]
  • mlpack was originally presented at the BigLearning workshop of NIPS 2011[5] and later published in the Journal of Machine Learning Research[6].
  • Dec 17, 2011, mlpack 1.0.0 was released.[7]
  • july 21st, 2016, mlpack 2.0.3 was released.[8]

Supported Algorithms

  • Collaborative Filtering
  • Density Estimation Trees
  • Euclidean Minimum Spanning Trees
  • Fast Exact Max-Kernel Search (FastMKS)
  • Gaussian Mixture Models (GMMs)
  • Hidden Markov Models (HMMs)Gaussian Mixture Models (GMMs)
  • Kernel Principal Component Analysis (KPCA)
  • K-Means Clustering
  • Least-Angle Regression (LARS/LASSO)
  • Local Coordinate Coding
  • Locality-Sensitive Hashing (LSH)
  • Logistic regression
  • Naive Bayes Classifier
  • Neighbourhood Components Analysis (NCA)
  • Non-negative Matrix Factorization (NMF)
  • Principal Components Analysis (PCA)
  • Independent component analysis (ICA)
  • Rank-Approximate Nearest Neighbor (RANN)
  • Simple Least-Squares Linear Regression (and Ridge Regression)
  • Sparse Coding
  • Tree-based Neighbor Search (all-k-nearest-neighbors, all-k-furthest-neighbors), using either kd-trees or cover trees
  • Tree-based Range Search

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