Probabilistic Classification Vector Machinehttp://mloss.orgUpdates and additions to Probabilistic Classification Vector MachineenTue, 10 Nov 2015 13:16:19 -0000Probabilistic Classification Vector Machine 0.22<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. </p> <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). </p> <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 -0000 methodsmachine learninglapacksparse learningalgorithmsparservmmulticorebayesiannonlinear classificationindefinite kernelsprobabilistic modelstructu