This paper proposes a novel framework for automatic text categorization problem based on the kernel density classifier. The overall goal is to tackle two main issues in automatic ...
Dwi Sianto Mansjur, Ted S. Wada, Biing-Hwang Juang
Abstract— The paper presents an efficient construction algorithm for obtaining sparse kernel density estimates based on a regression approach that directly optimizes model gener...
In this paper we introduce “clipping,” a new method of syntactic approximation which is motivated by and works in conjunction with a sound and decidable denotational model for...
: Web searching techniques have been investigated and implemented in many aspects. Particularly, in case of personalization, more important issue is how to manipulate the results r...
Chonggun Kim, JaeYoun Jung, Hyeon-Cheol Zin, Jason...
We present a novel approach to query reformulation which combines syntactic and semantic information by means of generalized Levenshtein distance algorithms where the substitution...
Amac Herdagdelen, Massimiliano Ciaramita, Daniel M...