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 1 April 2015

The paper about Incremental Probabilistic Classification Vector
Machine with linear costs was accepted at the International Joint Conference on Neural Networks. In the paper we improve the runtime and memory complexity of the Incremental Probabilistic Classification Vector Machine (EPCVM) to linear costs and derive a formulation for a Nystroem approximation on asymmetric matrices.

Machine learning for non-metric proximity data

Wednesday 1 April 2015

No comments:

Post a Comment