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 flexibility for expert users. MLPACK contains 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), Tree-based Range Search.
|License||Verified by||Verified on||Notes|
Leaders and contributors
Resources and communication
|Required to use||BLAS|
|Required to build||Armadillo C++ library|
|Required to use||LAPACK|
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