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Introduction to lattice algebra = wi...

Ritter, G. X.

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Introduction to lattice algebra = with applications in AI, pattern recognition, image analysis, and biomimetic neural networks /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Introduction to lattice algebra/ Gerhard X. Ritter, Gonzalo Urcid.
Reminder of title:	with applications in AI, pattern recognition, image analysis, and biomimetic neural networks /
Author:	Ritter, G. X.
other author:	Urcid, Gonzalo.
Published:	Boca Raton, FL :Chapman & Hall/CRC Press, : 2022.,
Description:	1 online resource :ill.
Subject:	Lattice theory. -
Online resource:	https://www.taylorfrancis.com/books/9781003154242
ISBN:	9781003154242

Introduction to lattice algebra = with applications in AI, pattern recognition, image analysis, and biomimetic neural networks /
Ritter, G. X.

Introduction to lattice algebrawith applications in AI, pattern recognition, image analysis, and biomimetic neural networks /[electronic resource] :Gerhard X. Ritter, Gonzalo Urcid. - 1st ed. - Boca Raton, FL :Chapman & Hall/CRC Press,2022. - 1 online resource :ill.

Includes bibliographical references and index.

Lattice theory extends into virtually every branch of mathematics, ranging from measure theory and convex geometry to probability theory and topology. A more recent development has been the rapid escalation of employing lattice theory for various applications outside the domain of pure mathematics. These applications range from electronic communication theory and gate array devices that implement Boolean logic to articial intelligence and computer science in general. Introduction to LatticeAlgebra: With Applications in AI, Pattern Recognition, Image Analysis, and Biomimetic Neural Networks lays emphasis on two subjects, the first being lattice algebra and the second the practical applications of that algebra. This textbook is intended to be used for a special topics course in articial intelligence with a focus on pattern recognition, multispectral image analysis, and biomimetic articial neural networks. The book is self-contained and - depending on the student's major - can be usedfor a senior undergraduate level or rst-year graduate level course. The book is also an ideal self-study guide for researchers and professionals in the above-mentioned disciplines. Features Filled with instructive examples and exercises to help build understanding Suitable for researchers, professionals and students, both in mathematics and computer science Every chapter consists of exercises with solution provided online at www.Routledge.com/9780367720292.

ISBN: 9781003154242Subjects--Topical Terms:

532024
Lattice theory.

LC Class. No.: QA76.9.L38 / R58 2021

Dewey Class. No.: 004.015113

Introduction to lattice algebra = with applications in AI, pattern recognition, image analysis, and biomimetic neural networks /
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245 1 0 $a Introduction to lattice algebra $h [electronic resource] : $b with applications in AI, pattern recognition, image analysis, and biomimetic neural networks / $c Gerhard X. Ritter, Gonzalo Urcid.
250 $a 1st ed.
260 $a Boca Raton, FL : $b Chapman & Hall/CRC Press, $c 2022.
300 $a 1 online resource : $b ill.
504 $a Includes bibliographical references and index.
520 $a Lattice theory extends into virtually every branch of mathematics, ranging from measure theory and convex geometry to probability theory and topology. A more recent development has been the rapid escalation of employing lattice theory for various applications outside the domain of pure mathematics. These applications range from electronic communication theory and gate array devices that implement Boolean logic to articial intelligence and computer science in general. Introduction to LatticeAlgebra: With Applications in AI, Pattern Recognition, Image Analysis, and Biomimetic Neural Networks lays emphasis on two subjects, the first being lattice algebra and the second the practical applications of that algebra. This textbook is intended to be used for a special topics course in articial intelligence with a focus on pattern recognition, multispectral image analysis, and biomimetic articial neural networks. The book is self-contained and - depending on the student's major - can be usedfor a senior undergraduate level or rst-year graduate level course. The book is also an ideal self-study guide for researchers and professionals in the above-mentioned disciplines. Features Filled with instructive examples and exercises to help build understanding Suitable for researchers, professionals and students, both in mathematics and computer science Every chapter consists of exercises with solution provided online at www.Routledge.com/9780367720292.
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650 0 $a Computer science $x Mathematics. $3 532725
650 0 $a Artificial intelligence $x Mathematical models. $3 811653
700 1 $a Urcid, Gonzalo. $3 3795140
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