Patterns can be any number of items that occur repeatedly, whether in the behaviour of animals, humans, traffic, or even in the appearance of a design. As technologies continue to advance, recognizing, mimicking, and responding to all types of patterns becomes more precise. Pattern Recognition and Classification in Time Series Data focuses on intelligent methods and techniques for recognizing and storing dynamic patterns. Emphasizing topics related to artificial intelligence, pattern management, and algorithm development, in addition to practical examples and applications, this publication is an essential reference source for graduate students, researchers, and professionals in a variety of computer-related disciplines.
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Eva Volna is an associate professor at the Department of Computer Science at University of Ostrava, Czech Republic. Her interests include artificial intelligence, artificial neural networks, evolutionary algorithms, and cognitive science. She is an author of more than 50 papers in technical journals and proceedings of conferences.
Martin Kotyrba is an assistant professor at the Department of Computer Science at the University of Ostrava, Czech Republic. His interests include artificial intelligence, formal logic, soft computing methods and fractals. He is an author of more than 30 papers in conference proceedings.
Michal Janosek is an assistant professor at the Department of Informatics and Computers at the University of Ostrava, Czech Republic. His interests include artificial intelligence, multi-agent systems, modeling and simulations. He is an author of more than 20 papers in proceedings of conferences.
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Gebunden. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Explores intelligent methods and techniques for recognizing and storing dynamic patterns. Emphasizing topics related to artificial intelligence, pattern management, and algorithm development, in addition to practical examples and applications, this publicat. N° de réf. du vendeur 448010039
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