Incremental Learning in Intelligent Systems - Theory and Applications

Part of the IEEE series on Computational Intelligence, this book offers the first text to cover both single task and multiple task incremental learning. The broad coverage encompasses both incremental learning theories and practical applications including pattern recognition, time-series prediction, outlier detection, adaptive robotics, bioinformatics, and biomedical diagnosis. Providing readers across multiple disciplines with a common foundation to work from, this book will prove an essential resource in efforts to develop new learning theories and algorithms, and in building new types of intelligent systems.

Zusammenfassung

* In the IEEE series of Computational Intelligence * First book to cover both a single task and multiple task incremental learning. Multiple task learning will become one of the key technologies in he machine learning fields. * Through coverage of increment learning theories.

Produktdetails

• Autoren: Nikola Kasabov, Nikola K. Kasabov, Seiichi Ozawa, Seiichi Pang Ozawa, Ozawa Seiichi, Shaoning Pang, Pang Shaoning
• Verlag: Wiley, John and Sons Ltd
• Sprache: Englisch
• Produktform: Fester Einband
• Erschienen: 01.10.2010
• EAN: 9780470879177
• ISBN: 978-0-470-87917-7
• Seiten: 350
• Serien: IEEE Press Series on Computational Intelligence; IEEE Press Series on Computational Intelligence
• Themen: Naturwissenschaften, Medizin, Informatik, Technik > Informatik, EDV > Allgemeines, Lexika; machine learning, COMPUTERS / Data Science / Machine Learning