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Fast Linear binary SVM classifier

version (94.3 KB) by Sebastien PARIS
Fast implementation of Linear binary SVM via BLAS/OpenMP API


Updated 29 Jan 2020

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LSVM v 3.0
Fast Linear SVM binary solver toolbox such PEGASOS/LIBLINEAR.
This toolbox offers fast implementation via mex-files of the two most
popular Linear SVM algorithms for binary classification: PEGASOS [1] and LIBLINEAR [2].

This toolbox can use BLAS/OpenMP API for faster computation on multi-cores processor.
It accepts dense inputs in single/double precision.

For comparaison with [2] in binary case, this package requires less memory and is approximatively between 10% up to 50% faster. Ideal for Large-scale training in computer vision for example


Run "mexme_lsvm.m" to compile mex-files.


Run "test_lsvm.m" for demo

Online help by typing pegasos_train or cddcsvm_train in matlab prompt.

References :
[1] S. Shalev-Shwartz, Y. Singer, and N. Srebro. "Pegasos: Primal estimated sub-GrAdient SOlver for SVM."
In Proc. ICML, 2007.
[2] Liblinear:

Cite As

Sebastien PARIS (2020). Fast Linear binary SVM classifier (, MATLAB Central File Exchange. Retrieved .

Comments and Ratings (3)

Sebastien PARIS

Venkat .... optimize your C be cross-validation

Venkat R

Hi Sebastien,
Thank you for sharing excellent software.
I am having training data of orders 9500 x 200000. Can you suggest some tips, if any on choice of algorithm/parameters.
I found cddcsvm_train, with C =5, B =1; better than PEGASOS.
But didn't know if any other choice of parameters-C or optimization technique can yield better results.

with regards,

Tianyang Ma

Sebastien, thanks for sharing such a great toolbox!


Fixed bugs & compatible with modern Matlab & OS64

- Fix a bug for single precision
- Fix a crash for large-scale data with OS64

-Cosmetic changes

MATLAB Release Compatibility
Created with R2018b
Compatible with any release
Platform Compatibility
Windows macOS Linux