We apply a well-known Bayesian probabilistic model to textual information retrieval: the classification of documents based on their relevance to a query. This model was previously...
According to some current thinking, a very large number of semantic concepts could provide researcher a novel way to characterize video and be utilized for video retrieval and und...
We present conditional random fields, a framework for building probabilistic models to segment and label sequence data. Conditional random fields offer several advantages over hid...
John D. Lafferty, Andrew McCallum, Fernando C. N. ...
Probabilistic feature relevance learning (PFRL) is an effective technique for adaptively computing local feature relevance for content-based image retrieval. It however becomes le...
Classical retrieval models support content-oriented searching for documents using a set of words as data model. However, in hypertext and database applications we want to consider...