mlpack
mlpack is a machine learning software library for C++, built on
top of the Armadillo library and the ensmallen (http://ensmallen.or
mlpack
g) numerical optimization library.[3] mlpack has 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.[4] Its intended target users are scientists and engineers.
Initial release February 1,
It is open-source software distributed under the BSD license,
making it useful for developing both open source and proprietary 2008[1]
software. Releases 1.0.11 and before were released under the Stable release 4.2.0[2] / 16
LGPL license. The project is supported by the Georgia Institute of June 2023
Technology and contributions from around the world.
Repository github.com
/mlpack
Miscellaneous features /mlpack (http
s://github.com/
Class templates for GRU, LSTM structures are available, thus the mlpack/mlpac
library also supports Recurrent Neural Networks. k)
There are bindings to R, Go, Julia,[5] and Python. Its binding Written in C++, Python,
system is extensible to other languages. Julia, Go
Operating system Cross-platform
Supported algorithms Available in English
Type Software
Currently mlpack supports the following algorithms and models:
library Machine
Collaborative Filtering learning
Decision stumps (one-level decision trees) License Open source
Density Estimation Trees (BSD)
Euclidean minimum spanning trees Website mlpack.org (htt
Gaussian Mixture Models (GMMs) ps://mlpack.or
Hidden Markov Models (HMMs) g)
Kernel density estimation (KDE)
Kernel Principal Component Analysis (KPCA)
K-Means Clustering
Least-Angle Regression (LARS/LASSO)
Linear Regression
Bayesian Linear Regression
Local Coordinate Coding
Locality-Sensitive Hashing (LSH)
Logistic regression
Max-Kernel Search
Naive Bayes Classifier
Nearest neighbor search with dual-tree algorithms
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, Sparse dictionary learning
Tree-based Neighbor Search (all-k-nearest-neighbors, all-k-furthest-neighbors), using either
kd-trees or cover trees
Tree-based Range Search
See also
Free and open-
source software
portal
Armadillo (C++ library)
List of numerical analysis software
List of numerical libraries
Numerical linear algebra
Scientific computing
References
1. "Initial checkin of the regression package to be released · mlpack/mlpack" (https://github.co
m/mlpack/mlpack/commit/6f0a3f7db2b4b30e208a89bf93dc2328294e4176). February 8,
2008. Retrieved May 24, 2020.
2. "Release 4.2.0" (https://github.com/mlpack/mlpack/releases/tag/4.2.0). 16 June 2023.
Retrieved 27 June 2023.
3. Ryan Curtin; et al. (2021). "The ensmallen library for flexible numerical optimization" (https://j
mlr.org/papers/v22/20-416.html). Journal of Machine Learning Research. 22 (166): 1–6.
arXiv:2108.12981 (https://arxiv.org/abs/2108.12981). Bibcode:2021arXiv210812981C (http
s://ui.adsabs.harvard.edu/abs/2021arXiv210812981C).
4. Ryan Curtin; et al. (2013). "mlpack: A Scalable C++ Machine Learning Library" (http://jmlr.or
g/papers/v14/curtin13a.html). Journal of Machine Learning Research. 14 (Mar): 801–805.
arXiv:1210.6293 (https://arxiv.org/abs/1210.6293). Bibcode:2012arXiv1210.6293C (https://ui.
adsabs.harvard.edu/abs/2012arXiv1210.6293C).
5. "Mlpack/Mlpack.jl" (https://github.com/mlpack/mlpack.jl). 10 June 2021.
External links
Official website (https://mlpack.org)
mlpack (https://github.com/mlpack/mlpack) on GitHub
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