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Ozkarahan, Esen – Information Processing & Management, 1995
This study develops an integrated conceptual representation scheme for multimedia documents that are viewed to comprise an object-oriented database; the necessary abstractions for the conceptual model and extensions to the relational model used as the search structure; a retrieval model that includes associative, semantic and media-specific…
Descriptors: Algorithms, Information Retrieval, Models, Multimedia Materials

Bookstein, Abraham; Klein, Shmuel T.; Raita, Timo – Information Processing & Management, 1997
Discussion of text compression focuses on a method to reduce the amount of storage needed to represent a Markov model with an extended alphabet, by applying a clustering scheme that brings together similar states. Highlights include probability vectors; algorithms; implementation details; and experimental data with natural languages. (Author/LRW)
Descriptors: Algorithms, Computer Science, Markov Processes, Models

Mostafa, J.; Lam, W. – Information Processing & Management, 2000
Presents a multilevel model of the information filtering process that permits document classification. Evaluates a document classification approach based on a supervised learning algorithm, measures the accuracy of the algorithm in a neural network that was trained to classify medical documents on cell biology, and discusses filtering…
Descriptors: Algorithms, Classification, Cytology, Evaluation Methods

Miyamoto, Sadaaki – Information Processing & Management, 2003
Proposes a fuzzy multiset model for information clustering with application to information retrieval on the World Wide Web. Highlights include search engines; term clustering; document clustering; algorithms for calculating cluster centers; theoretical properties concerning clustering algorithms; and examples to show how the algorithms work.…
Descriptors: Algorithms, Information Retrieval, Mathematical Formulas, Models

Boughanem, M.; Chrisment, C.; Soule-Dupuy, C. – Information Processing & Management, 1999
Presents a relevance-feedback strategy that improves the effectiveness of information-retrieval systems based on back-propagation of the relevance of retrieved documents using an algorithm developed in a neural approach. Describes a neural information-retrieval model and reports results obtained with the algorithm in three different environments.…
Descriptors: Algorithms, Information Retrieval, Mathematical Formulas, Models

Story, Roger E. – Information Processing & Management, 1996
Discussion of the use of Latent Semantic Indexing to determine relevancy in information retrieval focuses on statistical regression and Bayesian methods. Topics include keyword searching; a multiple regression model; how the regression model can aid search methods; and limitations of this approach, including complexity, linearity, and…
Descriptors: Algorithms, Difficulty Level, Indexing, Information Retrieval