A massive amount of content is available on web but huge portion of it is still invisible. User can only access this hidden web, also called Deep web, by entering a directed query in a web search form and thus accessing the data from database which is not indexed with hyperlinks. Inability to index particular type of content and restricted storage capacity is significant factor behind the invis...

We propose the use of latent space models applied to local invariant features for object classification. We investigate whether using latent space models enables to learn patterns of visual co-occurrence and if the learned visual models improve performance when less labeled data are available. We present and discuss results that support these hypotheses. Probabilistic Latent Semantic Analysis (...

This paper presents a new Bayesian non-parametric model by extending the usage of Hierarchical Latent Dirichlet Allocation to extract tree structured word clusters from text data. The inference algorithm of the model collects words in a cluster if they share similar distribution over documents. In our experiments, we observed meaningful hierarchical structures on NIPS corpus and radiology repor...

In this paper, an information theoretic approach for using word clusters in natural language call routing (NLCR) is proposed. This approach utilizes an automatic word class clustering algorithm to generate word classes from the word based training corpus. In our approach, the information gain (IG) based term selection is used to combine both word term and word class information in NLCR. A joint...

This paper presents the use of data clustering methods applied to the analysis results of a design-stage, functional failure reasoning tool. A system simulation using qualitative descriptions of component behaviors and a functional reasoning tool are used to identify the functional impact of a large set of potential single and multiple fault scenarios. The impact of each scenario is collected a...