Soft Computing in Information Retrieval: Techniques and Applications: Studies in Fuzziness and Soft Computing, cartea 50
Editat de Fabio Crestani, Gabriella Pasien Limba Engleză Paperback – 21 oct 2010
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Specificații
ISBN-13: 9783790824735
ISBN-10: 3790824739
Pagini: 408
Ilustrații: XII, 396 p.
Dimensiuni: 155 x 235 x 21 mm
Greutate: 0.57 kg
Ediția:Softcover reprint of hardcover 1st ed. 2000
Editura: Physica-Verlag HD
Colecția Physica
Seria Studies in Fuzziness and Soft Computing
Locul publicării:Heidelberg, Germany
ISBN-10: 3790824739
Pagini: 408
Ilustrații: XII, 396 p.
Dimensiuni: 155 x 235 x 21 mm
Greutate: 0.57 kg
Ediția:Softcover reprint of hardcover 1st ed. 2000
Editura: Physica-Verlag HD
Colecția Physica
Seria Studies in Fuzziness and Soft Computing
Locul publicării:Heidelberg, Germany
Public țintă
ResearchCuprins
I. Fuzzy Set Theory.- A Framework for Linguistic and Hierarchical Queries in Document Retrieval.- Application of Fuzzy Set Theory to Extend Boolean Information Retrieval.- A Model of Intelligent Information Retrieval Using Fuzzy Tolerance Relations Based on Hierarchical Co-Occurrence of Words.- II. Neural Networks.- Visual Keywords: from Text Retrieval to Multimedia Retrieval.- Document Classification with Unsupervised Artificial Neural Networks.- The Java Search Agent Workshop.- A Connectionist Approach to Content Access in Documents: Application to Detection of Jokes.- III. Genetic Algorithms.- Connectionist and Genetic Approaches for Information Retrieval.- Large Population or Many Generations for Genetic Algorithms? Implications in Information Retrieval.- IV. Evidential and Probabilistic Reasoning.- A Logical Information Retrieval Model Based on a Combination of Propositional Logic and Probability Theory.- Bayesian Network Models for Information Retrieval.- Probabilistic Learning by Uncertainty Sampling with Non-Binary Relevance.- V. Rough Sets Theory, Multivalued Logics, and Other Approaches.- Granulär Information Retrieval.- A Framework for the Retrieval of Multimedia Objects Based on Four-Valued Fuzzy Description Logics.- Rough and Fuzzy Sets for Data Mining of a Controlled Vocabulary for Textual Retrieval.- Rough Sets and Multisets in a Model of Information Retrieval.