Ordinal regression has become an effective way of learning user preferences, but most of research only focuses on single regression problem. In this paper we introduce collaborati...
Shipeng Yu, Kai Yu, Volker Tresp, Hans-Peter Krieg...
Background: Designing appropriate machine learning methods for identifying genes that have a significant discriminating power for disease outcomes has become more and more importa...
This paper presents an algorithm to estimate simultaneously both mean and variance of a non parametric regression problem. The key point is that we are able to estimate variance l...
We investigate the use of hierarchical Gaussian shortlists to speed up Gaussian likelihood computation. This approach is a combination of hierarchical Gaussian selection and stand...
The equivalent kernel [1] is a way of understanding how Gaussian process regression works for large sample sizes based on a continuum limit. In this paper we show (1) how to appro...