MLPACK (C++ library)
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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.
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 at Georgia Tech
- mlpack was originally presented at the BigLearning workshop of NIPS 2011 and later published in the Journal of Machine Learning Research.
- Dec 17, 2011, mlpack 1.0.0 was released.
- july 21st, 2016, mlpack 2.0.3 was released.
- 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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