MLPACK: a scalable C++ machine learning library

Authors:
Ryan R. Curtin;James R. Cline;N. P. Slagle;William B. March;Parikshit Ram;Nishant A. Mehta;Alexander G. Gray
Affiliations:
College of Computing, Georgia Institute of Technology, Atlanta, GA;College of Computing, Georgia Institute of Technology, Atlanta, GA;College of Computing, Georgia Institute of Technology, Atlanta, GA;College of Computing, Georgia Institute of Technology, Atlanta, GA;College of Computing, Georgia Institute of Technology, Atlanta, GA;College of Computing, Georgia Institute of Technology, Atlanta, GA;College of Computing, Georgia Institute of Technology, Atlanta, GA
Venue:
The Journal of Machine Learning Research
Year:
2013

Citing 7
Cited 0

ICA using spacings estimates of entropy

The Journal of Machine Learning Research
The WEKA data mining software: an update

ACM SIGKDD Explorations Newsletter
Fast euclidean minimum spanning tree: algorithm, analysis, and applications

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
The SHOGUN Machine Learning Toolbox

The Journal of Machine Learning Research
LIBSVM: A library for support vector machines

ACM Transactions on Intelligent Systems and Technology (TIST)
Density estimation trees

Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Scikit-learn: Machine Learning in Python

The Journal of Machine Learning Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

MLPACK is a state-of-the-art, scalable, multi-platform C++ machine learning library released in late 2011 offering both a simple, consistent API accessible to novice users and high performance and flexibility to expert users by leveraging modern features of C++. MLPACK provides cutting-edge algorithms whose benchmarks exhibit far better performance than other leading machine learning libraries. MLPACK version 1.0.3, licensed under the LGPL, is available at http://www.mlpack.org.