Classification and Learning Using Genetic Algorithms
Applications in Bioinformatics and Web Intelligence
(Sprache: Englisch)
This book provides a unified framework that describes how genetic learning can be used to design pattern recognition and learning systems. It examines how a search technique, the genetic algorithm, can be used for pattern classification mainly through...
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Klappentext zu „Classification and Learning Using Genetic Algorithms “
This book provides a unified framework that describes how genetic learning can be used to design pattern recognition and learning systems. It examines how a search technique, the genetic algorithm, can be used for pattern classification mainly through approximating decision boundaries. Coverage also demonstrates the effectiveness of the genetic classifiers vis-à-vis several widely used classifiers, including neural networks.
Inhaltsverzeichnis zu „Classification and Learning Using Genetic Algorithms “
- Introduction- Genetic Algorithms
- Supervised Classification Using Genetic Algorithms
- Theoretical Analysis of the GA-Classifier
- Variable String Lengths in GA-Classifier
- Chromosome Differentiation in VGA-Classifier
- Multi-objective VGA-Classifier and Quantitative Indices
- Genetic Algorithms in Clustering
- Genetic Learning in Bioinformatics
- Genetic Algorithms and Web Intelligence
- Appendices
- References
- Index
Autoren-Porträt von Sanghamitra Bandyopadhyay, Sankar Kumar Pal
Sankar K. Pal, PhD, is a Distinguished Scientist and founding head of the Machine Intelligence Unit at the Indian Statistical Institute, Calcutta. Professor Pal holds several PhDs and is a Fellow of the IEEE and IAPR.
Bibliographische Angaben
- Autoren: Sanghamitra Bandyopadhyay , Sankar Kumar Pal
- 2007, 2007, 311 Seiten, Masse: 16 x 24,1 cm, Gebunden, Englisch
- Verlag: Springer
- ISBN-10: 3540496068
- ISBN-13: 9783540496069
- Erscheinungsdatum: 23.04.2007
Sprache:
Englisch
Rezension zu „Classification and Learning Using Genetic Algorithms “
"This book tries to balance the mixture of theories, algorithms, and applications and is a good reference for people who want to solve a complex optimization problem for their field. ... Overall, this book is well organized and well written. There is no doubt that this is another good pattern recognition reference to have on one's bookshelf." (Zheng Liu, IAPR Newsletter 30(4), October 2008)
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