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Small Introduction about mlpack?

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What is Mlpack Library?

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.

As a result of this approach, mlpack outperforms competing machine learning libraries by large margins; see the BigLearning workshop paper and the benchmarks for details.

mlpack is developed by contributors from 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.)

mlpack was originally presented at the BigLearning workshop of NIPS 2011 [pdf] and later published in the Journal of Machine Learning Research [pdf], with version 3 being published in the Journal of Open Source Software [pdf]. Please cite mlpack in your work using this citation.

mlpack bindings for R are provided by the RcppMLPACK project.

Currently mlpack supports the following algorithms:

  • Collaborative Filtering
  • Decision stumps (one-level decision trees)
  • Density Estimation Trees
  • Euclidean Minimum Spanning Trees
  • Gaussian Mixture Models (GMMs)
  • Hidden Markov Models (HMMs)
  • Kernel Principal Component Analysis (KPCA)
  • K-Means Clustering
  • Least-Angle Regression (LARS/LASSO)
  • 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

For more detail visit here - http://mlpack.org/docs.html

Video for Mlpack

posted Nov 26 by anonymous

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