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04279nam a22005175i 4500 |
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978-3-540-72687-6 |
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DE-He213 |
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20210615204105.0 |
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100301s2007 gw | s |||| 0|eng d |
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|a 9783540726876
|9 978-3-540-72687-6
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|a 006.3
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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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|a 1st ed. 2007.
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|a Berlin, Heidelberg :
|b Springer Berlin Heidelberg :
|b Imprint: Springer,
|c 2007.
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|a XVI, 375 p.
|b online resource.
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|a text
|b txt
|2 rdacontent
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|a computer
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|a online resource
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|a text file
|b PDF
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|a Studies in Computational Intelligence,
|x 1860-949X ;
|v 67
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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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|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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|a Artificial intelligence.
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|a Applied mathematics.
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|a Engineering mathematics.
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|a Artificial Intelligence.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/I21000
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|a Mathematical and Computational Engineering.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/T11006
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| 700 |
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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 |
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|a Ritter, Gerhard X.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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|a SpringerLink (Online service)
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|t Springer Nature eBook
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|i Printed edition:
|z 9783540838630
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|i Printed edition:
|z 9783642091742
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|i Printed edition:
|z 9783540726869
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| 830 |
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|a Studies in Computational Intelligence,
|x 1860-949X ;
|v 67
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| 856 |
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|u https://doi.org/10.1007/978-3-540-72687-6
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|a ZDB-2-ENG
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|a ZDB-2-SXE
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|a Engineering (SpringerNature-11647)
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| 950 |
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|a Engineering (R0) (SpringerNature-43712)
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