LIBS2ML : a library for scalable second order machine learning algorithms

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Chauhan, Vinod Kumar and Sharma, Anuj and Dahiya, Kalpana (2021) LIBS2ML : a library for scalable second order machine learning algorithms. Software Impacts, 10. 100123. ISSN 2665-9638 (https://doi.org/10.1016/j.simpa.2021.100123)

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Abstract

Most of the machine learning libraries are either in MATLAB/Python/R which are very slow and not suitable for large-scale learning, or are in C/C++ which does not have easy ways to take input and display results. LIBS2ML1 has been developed using MEX files, i.e., C++ with MATLAB/Octave interface to take the advantage of faster learning using C++ and easy I/O using MATLAB/Octave. So, LIBS2ML is a completely unique due to its focus on the scalable second order methods – the hot research topic – and being based on MEX files. It provides researchers a comprehensive environment to evaluate their ideas and it also provides machine learning practitioners an effective tool to deal with the large-scale learning problems. LIBS2ML is an open-source, highly efficient, extensible, scalable, readable, portable and easy to use library.

ORCID iDs

Chauhan, Vinod Kumar ORCID logoORCID: https://orcid.org/0000-0001-8195-548X, Sharma, Anuj and Dahiya, Kalpana;
CORE (COnnecting REpositories)