We introduce a boosting framework to solve a classification problem with added manifold and ambient regularization costs. It allows for a natural extension of boosting into both s...
Nicolas Loeff, David A. Forsyth, Deepak Ramachandr...
An extendable and generic Agent Enriched Data Mining (AEDM) framework, EMADS (the Extendable Multi-Agent Data mining System) is described. The central feature of the framework is ...
This paper describes a probabilistic answer selection framework for question answering. In contrast with previous work using individual resources such as ontologies and the Web to...
This paper describes the development of a framework to build Virtual Communities of Practice in the web. It is part of a larger project named as DWeb (acronym for Dream Web). The p...
This paper presents a novel metric-based framework for the task of automatic taxonomy induction. The framework incrementally clusters terms based on ontology metric, a score indic...