Classification and Learning Using Genetic Algorithms Applications in Bioinformatics and Web Intelligence /

This book provides a unified framework that describes how genetic learning can be used to design pattern recognition and learning systems. The book is unique in the sense of describing how a search technique, the genetic algorithm, can be used for pattern classification mainly through approximating...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριοι συγγραφείς: Bandyopadhyay, Sanghamitra (Συγγραφέας), Pal, Sankar K. (Συγγραφέας)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2007.
Σειρά:Natural Computing Series,
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
LEADER 03445nam a22005895i 4500
001 978-3-540-49607-6
003 DE-He213
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024 7 |a 10.1007/3-540-49607-6  |2 doi 
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100 1 |a Bandyopadhyay, Sanghamitra.  |e author. 
245 1 0 |a Classification and Learning Using Genetic Algorithms  |h [electronic resource] :  |b Applications in Bioinformatics and Web Intelligence /  |c by Sanghamitra Bandyopadhyay, Sankar K. Pal. 
264 1 |a Berlin, Heidelberg :  |b Springer Berlin Heidelberg,  |c 2007. 
300 |a XVI, 311 p. 87 illus.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
347 |a text file  |b PDF  |2 rda 
490 1 |a Natural Computing Series,  |x 1619-7127 
505 0 |a Genetic Algorithms -- Supervised Classification Using Genetic Algorithms -- Theoretical Analysis of the GA-classifier -- Variable String Lengths in GA-classifier -- Chromosome Differentiation in VGA-classifier -- Multiobjective VGA-classifier and Quantitative Indices -- Genetic Algorithms in Clustering -- Genetic Learning in Bioinformatics -- Genetic Algorithms and Web Intelligence. 
520 |a This book provides a unified framework that describes how genetic learning can be used to design pattern recognition and learning systems. The book is unique in the sense of describing how a search technique, the genetic algorithm, can be used for pattern classification mainly through approximating decision boundaries, and it demonstrates the effectiveness of the genetic classifiers vis-à-vis several widely used classifiers, including neural networks. It provides a balanced mixture of theories, algorithms and applications, and in particular results from the bioinformatics and Web intelligence domains. This book will be useful to graduate students and researchers in computer science, electrical engineering, systems science, and information technology, both as a text and reference book. Researchers and practitioners in industry working in system design, control, pattern recognition, data mining, soft computing, bioinformatics and Web intelligence will also benefit. 
650 0 |a Computer science. 
650 0 |a Computer programming. 
650 0 |a Artificial intelligence. 
650 0 |a Pattern recognition. 
650 0 |a Bioinformatics. 
650 0 |a Statistical physics. 
650 0 |a Dynamical systems. 
650 0 |a Electrical engineering. 
650 1 4 |a Computer Science. 
650 2 4 |a Pattern Recognition. 
650 2 4 |a Programming Techniques. 
650 2 4 |a Communications Engineering, Networks. 
650 2 4 |a Artificial Intelligence (incl. Robotics). 
650 2 4 |a Statistical Physics, Dynamical Systems and Complexity. 
650 2 4 |a Computational Biology/Bioinformatics. 
700 1 |a Pal, Sankar K.  |e author. 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer eBooks 
776 0 8 |i Printed edition:  |z 9783540496069 
830 0 |a Natural Computing Series,  |x 1619-7127 
856 4 0 |u http://dx.doi.org/10.1007/3-540-49607-6  |z Full Text via HEAL-Link 
912 |a ZDB-2-SCS 
950 |a Computer Science (Springer-11645)