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Introduction to Lattice Algebra : With Applications in AI, Pattern Recognition, Image Analysis, and Biomimetic Neural Networks (Chapman & Hall/CRC Mathematics and Artificial Intelligence Series)

By: Gerhard X. Ritter (Author) , Gonzalo Urcid (Author)

Manufacture on Demand

Ksh 27,850.00

Format: Hardback or Cased Book

ISBN-10: 0367720299

ISBN-13: 9780367720292

Collection / Series: Chapman & Hall/CRC Mathematics and Artificial Intelligence Series

Collection Type: Publisher collection

Publisher: Taylor & Francis Ltd

Imprint: Chapman & Hall/CRC

Country of Manufacture: GB

Country of Publication: GB

Publication Date: Aug 24th, 2021

Publication Status: Active

Product extent: 418 Pages

Weight: 852.00 grams

Dimensions (height x width x thickness): 16.20 x 24.10 x 2.80 cms

Product Classification / Subject(s): Algebra
Automatic control engineering
Artificial intelligence

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  • Description

  • Reviews

This book 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 arti?cial intelligence with focus on pattern recognition, multispectral image analysis, and biomimetic arti?cial neural networks.

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 artificial intelligence and computer science in general.

Introduction to Lattice Algebra: 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 artificial intelligence with a focus on pattern recognition, multispectral image analysis, and biomimetic artificial neural networks. The book is self-contained and – depending on the student’s major – can be used for a senior undergraduate level or first-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
    • Contains numerous exercises.

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