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This title in other editions

Studies in Computational Intelligence #27: Towards a Unified Modeling and Knowledge-Representation Based on Lattice Theory

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Studies in Computational Intelligence #27: Towards a Unified Modeling and Knowledge-Representation Based on Lattice Theory Cover

 

Synopses & Reviews

Publisher Comments:

By 'model' we mean a mathematical description of a world aspect. With the proliferation of computers a variety of modeling paradigms emerged under computational intelligence and soft computing. An advancing technology is currently fragmented due, as well, to the need to cope with different types of data in different application domains. This research monograph proposes a unified, cross-fertilizing approach for knowledge-representation and modeling based on lattice theory. The emphasis is on clustering, classification, and regression applications. It is shown how rigorous analysis and design can be pursued in soft computing using conventional (hard computing) methods. Moreover, non-Turing computation can be pursued. The material here is multi-disciplinary based on our on-going research published in major scientific journals and conferences. Experimental results by various algorithms are demonstrated extensively. Relevant work by other authors is also presented both extensively and comparatively.

Synopsis:

This research monograph proposes a unified, cross-fertilizing approach for knowledge-representation and modeling based on lattice theory. The emphasis is on clustering, classification, and regression applications. It presents novel tools and useful perspectives for effective pattern classification. The material is multi-disciplinary based on on-going research published in major scientific journals and conferences.

Table of Contents

Part I: The Context.- Origins in Context.- Relevant Literature Review.- Part II: Theory and Algorithms.- Novel Mathematical Background.- Real-World Grounding.- Knowledge Representation.- The Modeling Problem and its Formulation.- Algorithms for Clustering, Classification, and Regression.- Part III: Applications and Comparisons.- Numeric Data Applications Nonnumeric Data Applications.- Connections with Established Paradigms.- Part IV: Conclusion.- Implementation Issues.- Discussion.

Product Details

ISBN:
9783540341697
Author:
Kaburlasos, Vassilis G.
Publisher:
Springer
Subject:
Computational intelligence
Subject:
Soft computing
Subject:
Applied
Subject:
Intelligence (AI) & Semantics
Subject:
Classification
Subject:
clustering
Subject:
Function Estimation
Subject:
Fuzzy Inference
Subject:
Knowledge-Representationa
Subject:
Machine learning
Subject:
system modeling
Subject:
Appl.Mathematics/Computational Methods of Engineering
Subject:
Artificial Intelligence (incl. Robotics)
Subject:
APPLICATIONS OF MATHEMATICS
Subject:
Applications of Mathematics <P>Presents novel tools and useful perspectives for effective pattern classification and function approximation problems based on disparate types of data</P> <P></P> <P>Introduces useful novel tools, which have the potential to
Subject:
Mathematics-Applied
Copyright:
Edition Description:
Book
Series:
Studies in Computational Intelligence
Series Volume:
27
Publication Date:
20060728
Binding:
HARDCOVER
Language:
English
Illustrations:
Y
Pages:
267
Dimensions:
235 x 155 mm 1220 gr

Related Subjects

Computers and Internet » Artificial Intelligence » Fuzzy Logic
Computers and Internet » Artificial Intelligence » General
Computers and Internet » Computers Reference » General
Engineering » Engineering » General Engineering
History and Social Science » Linguistics » Specific Languages and Groups
Reference » Words Phrases and Language
Science and Mathematics » Mathematics » Applied
Science and Mathematics » Mathematics » General
Science and Mathematics » Physics » General

Studies in Computational Intelligence #27: Towards a Unified Modeling and Knowledge-Representation Based on Lattice Theory New Hardcover
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Product details 267 pages Springer - English 9783540341697 Reviews:
"Synopsis" by , This research monograph proposes a unified, cross-fertilizing approach for knowledge-representation and modeling based on lattice theory. The emphasis is on clustering, classification, and regression applications. It presents novel tools and useful perspectives for effective pattern classification. The material is multi-disciplinary based on on-going research published in major scientific journals and conferences.
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