Hidden Markov Models (HMM) are probabilistic graphical models for interdependent classification. In this paper we experiment with different ways of combining the components of an ...
The goal of this work is to integrate query similarity metrics as features into a dense model that can be trained on large amounts of query log data, in order to rank query rewrit...
Fabio De Bona, Stefan Riezler, Keith Hall, Massimi...
This paper describes an integrated model for the realization of an Open and Distance Learning (ODL) environment supporting traditional learning procedures, through collaborative le...
We present a statistical model for organizing image collections which integrates semantic information provided by associated text and visual information provided by image features...
Mappings to structured output spaces (strings, trees, partitions, etc.) are typically learned using extensions of classification algorithms to simple graphical structures (eg., li...