MLPACK (C++ library)
| Stable release | 2.0.0 / December 23, 2015 |
|---|---|
| Written in | C++ |
| Operating system | Cross-platform |
| Available in | English |
| Type | Software library |
| License | Open source (BSD) |
| Website | mlpack |
MLPACK is a machine learning software library for C++, built on top of the Armadillo library. 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.[1] Its intended target users are scientists and engineers.
It is open-source software distributed under the BSD license, making it useful for developing both open source and proprietary software. Releases 1.0.11 and before were released under the LGPL license. The project is supported by the Georgia Institute of Technology and contributions from around the world.
Supported algorithms
Currently MLPACK supports the following algorithms:
- Collaborative Filtering
- Density Estimation Trees
- Euclidean Minimum Spanning Trees
- Fast Exact Max-Kernel Search (FastMKS)
- Gaussian Mixture Models (GMMs)
- Hidden Markov Models (HMMs)
- 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
See also
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- Numerical linear algebra
- List of numerical libraries
- List of numerical analysis software
- Scientific computing
- Armadillo (C++ library)
References
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