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|a 9783540726876
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|a 10.1007/978-3-540-72687-6
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|a (DE-He213)978-3-540-72687-6
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|a Computational Intelligence Based on Lattice Theory
|h [electronic resource] /
|c edited by Vassilis G. Kaburlasos, Gerhard X. Ritter.
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| 250 |
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|a 1st ed. 2007.
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| 264 |
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1 |
|a Berlin, Heidelberg :
|b Springer Berlin Heidelberg :
|b Imprint: Springer,
|c 2007.
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| 300 |
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|a XVI, 375 p.
|b online resource.
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| 336 |
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|a text
|b txt
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| 490 |
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|a Studies in Computational Intelligence,
|x 1860-949X ;
|v 67
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| 490 |
1 |
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|a Springer eBook Collection
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| 505 |
0 |
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|a Neural Computation -- Granular Enhancement of Fuzzy ART/SOM Neural Classifiers Based on Lattice Theory -- Learning in Lattice Neural Networks that Employ Dendritic Computing -- Orthonormal Basis Lattice Neural Networks -- Generalized Lattices Express Parallel Distributed Concept Learning -- Mathematical Morphology Applications -- Noise Masking for Pattern Recall Using a Single Lattice Matrix Associative Memory -- Convex Coordinates From Lattice Independent Sets for Visual Pattern Recognition -- A Lattice-Based Approach to Mathematical Morphology for Greyscale and Colour Images -- Morphological and Certain Fuzzy Morphological Associative Memories for Classification and Prediction -- Machine Learning Applications -- The Fuzzy Lattice Reasoning (FLR) Classifier for Mining Environmental Data -- Machine Learning Techniques for Environmental Data Estimation -- Application of Fuzzy Lattice Neurocomputing (FLN) in Ocean Satellite Images for Pattern Recognition -- Genetically Engineered ART Architectures -- Fuzzy Lattice Reasoning (FLR) Classification Using Similarity Measures -- Logic and Inference -- Fuzzy Prolog: Default Values to Represent Missing Information -- Valuations on Lattices: Fuzzification and its Implications -- L-fuzzy Sets and Intuitionistic Fuzzy Sets -- A Family of Multi-valued t-norms and t-conorms -- The Construction of Fuzzy-valued t-norms and t-conorms.
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| 520 |
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|a The emergence of lattice theory within the field of computational intelligence (CI) is partially due to its proven effectiveness in neural computation. Moreover, lattice theory has the potential to unify a number of diverse concepts and aid in the cross-fertilization of both tools and ideas within the numerous subfields of CI. The compilation of this eighteen-chapter book is an initiative towards proliferating established knowledge in the hope to further expand it. This edited book is a balanced synthesis of four parts emphasizing, in turn, neural computation, mathematical morphology, machine learning, and (fuzzy) inference/logic. The articles here demonstrate how lattice theory may suggest viable alternatives in practical clustering, classification, pattern analysis, and regression applications.
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| 590 |
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|a Loaded electronically.
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| 590 |
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|a Electronic access restricted to members of the Holy Cross Community.
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| 650 |
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|a Artificial intelligence.
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| 650 |
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|a Applied mathematics.
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| 650 |
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|a Engineering mathematics.
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| 690 |
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|a Electronic resources (E-books)
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| 700 |
1 |
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|a Kaburlasos, Vassilis G.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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| 700 |
1 |
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|a Ritter, Gerhard X.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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| 710 |
2 |
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|a SpringerLink (Online service)
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| 773 |
0 |
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|t Springer eBooks
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| 830 |
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|a Studies in Computational Intelligence,
|x 1860-949X ;
|v 67
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| 830 |
|
0 |
|a Springer eBook Collection.
|
| 856 |
4 |
0 |
|u https://holycross.idm.oclc.org/login?auth=cas&url=https://doi.org/10.1007/978-3-540-72687-6
|3 Click to view e-book
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