Transactions on Rough Sets V [electronic resource] / edited by James F. Peters, Andrzej Skowron.
Material type: TextSeries: Lecture Notes in Computer Science ; 4100Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2006Description: X, 507 p. Also available online. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783540393832Subject(s): Computer science | Database management | Artificial intelligence | Computer vision | Computer Science | Mathematical Logic and Formal Languages | Computation by Abstract Devices | Artificial Intelligence (incl. Robotics) | Database Management | Image Processing and Computer VisionAdditional physical formats: Printed edition:: No titleDDC classification: 005.131 LOC classification: QA8.9-QA10.3Online resources: Click here to access onlineCurrent library | Home library | Call number | Materials specified | URL | Status | Date due | Barcode |
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IMSc Library | IMSc Library | Link to resource | Available | EBK4634 |
Commemoration -- Zdzis?aw Pawlak: Life and Work -- Regular Papers -- Rough Belief Change -- Rough Sets and Vague Concept Approximation: From Sample Approximation to Adaptive Learning -- Matching 2D Image Segments with Genetic Algorithms and Approximation Spaces -- An Efficient Algorithm for Inference in Rough Set Flow Graphs -- Intelligent Algorithms for Movie Sound Tracks Restoration -- Rough Set-Based Application to Recognition of Emotionally-Charged Animated Character’s Gestures -- Introducing a Rule Importance Measure -- Variable Precision Bayesian Rough Set Model and Its Application to Kansei Engineering -- P300 Wave Detection Based on Rough Sets -- Multimodal Classification: Case Studies -- Arrow Decision Logic for Relational Information Systems -- On Generalized Rough Fuzzy Approximation Operators -- Rough Set Approximations in Formal Concept Analysis -- Motion-Information-Based Video Retrieval System Using Rough Pre-classification -- Dissertations and Monographs -- Approximate Boolean Reasoning: Foundations and Applications in Data Mining.
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