LIBS2ML: A library for scalable second order machine learning algorithms
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...
| Main Authors: | , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Elsevier
2021
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| _version_ | 1826309709103104000 |
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| author | Chauhan, VK Sharma, A Dahiya, K |
| author_facet | Chauhan, VK Sharma, A Dahiya, K |
| author_sort | Chauhan, VK |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-07T07:39:46Z |
| format | Journal article |
| id | oxford-uuid:b293c5bd-0481-4bc2-af72-c0cc766f98b3 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T07:39:46Z |
| publishDate | 2021 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:b293c5bd-0481-4bc2-af72-c0cc766f98b32023-04-17T10:32:14ZLIBS2ML: A library for scalable second order machine learning algorithmsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:b293c5bd-0481-4bc2-af72-c0cc766f98b3EnglishSymplectic ElementsElsevier2021Chauhan, VKSharma, ADahiya, KMost 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. |
| spellingShingle | Chauhan, VK Sharma, A Dahiya, K LIBS2ML: A library for scalable second order machine learning algorithms |
| title | LIBS2ML: A library for scalable second order machine learning algorithms |
| title_full | LIBS2ML: A library for scalable second order machine learning algorithms |
| title_fullStr | LIBS2ML: A library for scalable second order machine learning algorithms |
| title_full_unstemmed | LIBS2ML: A library for scalable second order machine learning algorithms |
| title_short | LIBS2ML: A library for scalable second order machine learning algorithms |
| title_sort | libs2ml a library for scalable second order machine learning algorithms |
| work_keys_str_mv | AT chauhanvk libs2mlalibraryforscalablesecondordermachinelearningalgorithms AT sharmaa libs2mlalibraryforscalablesecondordermachinelearningalgorithms AT dahiyak libs2mlalibraryforscalablesecondordermachinelearningalgorithms |