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 18 June 2014

Poster presentation about non-metric data analysis @MiWoCi/WSOM'14

I will have a poster presentation at MiWoCi / Workshop on Self-Organizing Maps 2014
http://www.global.hs-mittweida.de/~wsom2014
The poster will address different approaches to deal with indefinite / non-psd / non-positive
proximity data.

Machine learning for non-metric proximity data

Wednesday 18 June 2014

Poster presentation about non-metric data analysis @MiWoCi/WSOM'14

No comments:

Post a Comment