Machine learning for non-metric proximity data

This blog provides literature, algorithms and data sets for the analysis of (indefinite) proximity data. In machine learning kernels are given as proximity data. But if the proximity measure is non-metric most kernel approaches are inaccurate or fail. This blog shows ways how to deal with these so called indefinite, non-positive or non-psd proximity data, providing links to literature and algorithms. The final objective is to provide - Probabilistic Models in Pseudo-Euclidean Spaces (ProMoS)

Wednesday, 21 January 2015

Accepted paper at ESANN'2015

The paper about Probabilistic Classification Vector Machine at large scale was accepted as
oral presentation at the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. In the paper we present an alternative formulation of the Probabilistic Classification Vector Machine (PCVM) to achieve linear runtime complexity.

Machine learning for non-metric proximity data

Wednesday, 21 January 2015

Accepted paper at ESANN'2015

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