In modern databases, complex objects like multimedia data, proteins or text objects can be modeled in a variety of representations and can be decomposed into multiple instances of ...
Stefan Brecheisen, Hans-Peter Kriegel, Matthias Sc...
The hierarchical Dirichlet process hidden Markov model (HDP-HMM) is a flexible, nonparametric model which allows state spaces of unknown size to be learned from data. We demonstra...
Emily B. Fox, Erik B. Sudderth, Michael I. Jordan,...
In this paper, we integrate type-2 (T2) fuzzy sets with Markov random fields (MRFs) referred to as T2 FMRFs, which may handle both fuzziness and randomness in the structural patter...
A new hierarchical nonparametric Bayesian model is proposed for the problem of multitask learning (MTL) with sequential data. Sequential data are typically modeled with a hidden M...
The state of the art for large database object retrieval in images is based on quantizing descriptors of interest points into visual words. High similarity between matching image r...