Project details for Probabilistic Classification Vector Machine

Screenshot Probabilistic Classification Vector Machine 0.22

by fmschleif - November 10, 2015, 13:16:19 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ]

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Description:

Implementation of the Probabilistic Classification Vector Machine (PCVM)in acc. to the paper of Huanhuan Chen et al. The code contains a full library implementation including test code which can be similarly used like the libsvm. Thanks to the used armadillo / boost framework it supports multicore calculations and runs very fast using the numerical lapack libraries.

The PCVM is similar to the Relevance Vector Machine (RVM) of Tipping but with a more appropriate probabilistic model (see paper). The classifier can be used for potentially indefinite input kernels so is directly (and valid) applicable for non-metric input similarities e.g. as obtained from sequence alignment data or using shape measurements. The code provides a recent extension for a Nystroem approximated PCVM such that it also scales to larger scale problems and has finally a linear runtime complexity (if we fix the number of landmarks).

The code contains some simple examples shown in the Readme and comes with a command line interface, model input / output and a crossvalidation. It can also be used for own input kernel matrices. Default implemented kernels are a linear kernel and an extreme learning machine (elm) kernel.

Changes to previous version:

30.10.2015 * code has been revised in some places fixing also some errors different multiclass schemes and hdf5 file support added. Some speed ups and memory savings by better handling of intermediate objects.

27.05.2015: - Matlab binding under Windows available. Added a solution file for VS'2013 express to compile a matlab mex binding. Can not yet confirm that under windows the code is really using multiple cores (under linux it does)

29.04.2015 * added an implementation of the Nystroem based PCVM includes: Nystroem based singular value decomposition (SVD), eigenvalue decomposition (EVD) and pseudo-inverse calculation (PINV)

22.04.2015 * implementation of the PCVM released

BibTeX Entry: Download
Corresponding Paper BibTeX Entry: Download
URL: Project Homepage
Supported Operating Systems: Linux, Windows
Data Formats: Csv
Tags: Kernel, Classification, Kernel Methods, Machine Learning, Lapack, Sparse Learning, Algorithm, Sparse, Rvm, Multicore, Bayesian, Nonlinear Classification, Indefinite Kernels, Probabilistic Model, Structu
Archive: download here

Other available revisons

Version Changelog Date
0.22

30.10.2015 * code has been revised in some places fixing also some errors different multiclass schemes and hdf5 file support added. Some speed ups and memory savings by better handling of intermediate objects.

27.05.2015: - Matlab binding under Windows available. Added a solution file for VS'2013 express to compile a matlab mex binding. Can not yet confirm that under windows the code is really using multiple cores (under linux it does)

29.04.2015 * added an implementation of the Nystroem based PCVM includes: Nystroem based singular value decomposition (SVD), eigenvalue decomposition (EVD) and pseudo-inverse calculation (PINV)

22.04.2015 * implementation of the PCVM released

November 10, 2015, 13:16:19
0.21

30.10.2015 * code has been revised in some places fixing also some errors different multiclass schemes and hdf5 file support added. Some speed ups and memory savings by better handling of intermediate objects.

27.05.2015: - Matlab binding under Windows available. Added a solution file for VS'2013 express to compile a matlab mex binding. Can not yet confirm that under windows the code is really using multiple cores (under linux it does)

29.04.2015 * added an implementation of the Nystroem based PCVM includes: Nystroem based singular value decomposition (SVD), eigenvalue decomposition (EVD) and pseudo-inverse calculation (PINV)

22.04.2015 * implementation of the PCVM released

May 26, 2015, 16:24:17
0.2

Works now also with Matlab (see Readme - really - how it must be used)

April 29, 2015, 14:49:06

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