Genetic Algorithms for Pattern Recognition - Info and Reading Options
By Sankar K. Pal and Paul P. Wang
"Genetic Algorithms for Pattern Recognition" was published by Taylor & Francis Group in 2017 - Boca Raton, FL, it has 336 pages and the language of the book is English.
“Genetic Algorithms for Pattern Recognition” Metadata:
- Title: ➤ Genetic Algorithms for Pattern Recognition
- Authors: Sankar K. PalPaul P. Wang
- Language: English
- Number of Pages: 336
- Publisher: Taylor & Francis Group
- Publish Date: 2017
- Publish Location: Boca Raton, FL
“Genetic Algorithms for Pattern Recognition” Subjects and Themes:
- Subjects: ➤ Pattern perception - Genetic algorithms - Machine learning - COMPUTERS - General - Computer Graphics - Programming - Systems Analysis & Design - MATHEMATICS - Applied - Algorithmus - Genetischer Algorithmus - Mustererkennung
Edition Identifiers:
- The Open Library ID: OL28891245M - OL17976035W
- Online Computer Library Center (OCLC) ID: 1014359346
- ISBN-13: 9781138105577 - 9780203713402 - 9781351364492 - 9781351364485 - 9781351364478
- All ISBNs: 9781138105577 - 9780203713402 - 9781351364492 - 9781351364485 - 9781351364478
AI-generated Review of “Genetic Algorithms for Pattern Recognition”:
"Genetic Algorithms for Pattern Recognition" Description:
Open Data:
"Solving pattern recognition problems involves an enormous amount of computational effort. By applying genetic algorithms - a computational method based on the way chromosomes in DNA recombine - these problems are more efficiently and more accurately solved. Genetic Algorithms for Pattern Recognition covers a broad range of applications in science and technology, describing the integration of genetic algorithms in pattern recognition and machine learning problems to build intelligent recognition systems. The articles, written by leading experts from around the world, accomplish several objectives: they provide insight into the theory of genetic algorithms; they develop pattern recognition theory in light of genetic algorithms; and they illustrate applications in artificial neural networks and fuzzy logic. The cross-sectional view of current research presented in Genetic Algorithms for Pattern Recognition makes it a unique text, ideal for graduate students and researchers."--Provided by publisher
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