mloss.org Probabilistic Classification Vector Machinehttp://mloss.orgUpdates and additions to Probabilistic Classification Vector MachineenTue, 10 Nov 2015 13:16:19 -0000Probabilistic Classification Vector Machine 0.22http://mloss.org/software/view/610/<html><p>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.
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<p>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).
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<p>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.
</p></html>Frank Michael Schleif, Peter Tino, Huanhuan ChenTue, 10 Nov 2015 13:16:19 -0000http://mloss.org/software/rss/comments/610http://mloss.org/software/view/610/kernelclassificationkernel methodsmachine learninglapacksparse learningalgorithmsparservmmulticorebayesiannonlinear classificationindefinite kernelsprobabilistic modelstructu