In this paper, we study an information theoretic approach to image similarity measurement for content-base image retrieval. In this novel scheme, similarities are measured by the amount of information the images contained about one another – mutual information (MI). The given approach is based on the premise that two similar images should have high mutual information, or equivalently, the query...

There is a need for modeling and reasoning on the subjective, incomplete, imprecise and vague nature of item features, user behavior and their relationships in machine learning algorithms. This paper presents results of analysis of effects of fuzzy theoretic inference/recommendation strategies and fuzzy theoretic similarity measures on a recommender system's accuracy-measured in terms of precis...

Representation of features of items and user feedback, and reasoning about their relationships are major problems in recommender systems. This is because item features and user feedback are subjective, imprecise and vague. The paper presents a fuzzy set theoretic method (FTM) for recommender systems that handles the non-stochastic uncertainty induced from subjectivity, vagueness and imprecision...

This paper explores the problem of computing text similarity between verb phrases describing skilled human behavior for the purpose of finding approximate matches. Four parsers are evaluated on a large corpus of skill statements extracted from an enterprise-wide expertise taxonomy. A similarity measure utilizing common semantic role features extracted from parse trees was found superior to an i...

Decision-theoretic rough set theory (DTRS) is becoming one of the important research directions for studying set approximations using Bayesian decision procedure and probability theory in rough set community. In this paper, a novel model, fuzzy multigranulation decision-theoretic rough set model (FM-DTRS), is proposed in terms of inclusion measure of fuzzy rough sets in the viewpoint of fuzzy m...