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)

Monday, 2 February 2015

Tutorial at IJCNN 2015: Learning in indefinite proximity spaces: Mathematical foundations, representations, and models

I will present a tutorial about Learning in indefinite proximity spaces:
Mathematical foundations, representations, and models. The announcement
and content description of the tutorial is available here
Full material will follow in the next weeks.

Machine learning for non-metric proximity data

Monday, 2 February 2015

Tutorial at IJCNN 2015: Learning in indefinite proximity spaces: Mathematical foundations, representations, and models

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