Buch, Englisch, 311 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 664 g
Reihe: Natural Computing Series
Applications in Bioinformatics and Web Intelligence
Buch, Englisch, 311 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 664 g
Reihe: Natural Computing Series
ISBN: 978-3-540-49606-9
Verlag: Springer Berlin Heidelberg
This book provides a unified framework that describes how genetic learning can be used to design pattern recognition and learning systems. It provides a balanced mixture of theories, algorithms and applications, and in particular results from the bioinformatics and Web intelligence domains. The 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.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Naturwissenschaften Biowissenschaften Biowissenschaften
- Naturwissenschaften Biowissenschaften Angewandte Biologie Bioinformatik
- Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Robotik
- Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Grafikprogrammierung
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenkompression, Dokumentaustauschformate
- Mathematik | Informatik EDV | Informatik Angewandte Informatik Bioinformatik
- Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Algorithmen & Datenstrukturen
- Mathematik | Informatik EDV | Informatik Informatik Logik, formale Sprachen, Automaten
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
Weitere Infos & Material
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.
