Published by Berlin/ Heidelberg, Springer Berlin., 2001
ISBN 10: 3540415971 ISBN 13: 9783540415978
Language: English
Seller: Universitätsbuchhandlung Herta Hold GmbH, Berlin, Germany
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Add to basket2001. 16 x 24 cm. XII, 396 S. XII, 396 p. Softcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. (Lecture Notes in Computer Science; Lecture Notes in Artificial Intelligence). Sprache: Englisch.
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Paperback. Condition: Very Good. Ex-library paperback in very nice condition with the usual markings and attachments. Text block clean and unmarked. Tight binding.
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Paperback or Softback. Condition: New. Sequence Learning: Paradigms, Algorithms, and Applications 1.27. Book.
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Published by Springer Berlin Heidelberg, 2001
ISBN 10: 3540415971 ISBN 13: 9783540415978
Language: English
Seller: Buchpark, Trebbin, Germany
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Add to basketCondition: Sehr gut. Zustand: Sehr gut | Seiten: 408 | Sprache: Englisch | Produktart: Bücher.
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Published by Springer Berlin Heidelberg, 2001
ISBN 10: 3540415971 ISBN 13: 9783540415978
Language: English
Seller: AHA-BUCH GmbH, Einbeck, Germany
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Add to basketTaschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Sequential behavior is essential to intelligence in general and a fundamental part of human activities, ranging from reasoning to language, and from everyday skills to complex problem solving. Sequence learning is an important component of learning in many tasks and application fields: planning, reasoning, robotics natural language processing, speech recognition, adaptive control, time series prediction, financial engineering, DNA sequencing, and so on. This book presents coherently integrated chapters by leading authorities and assesses the state of the art in sequence learning by introducing essential models and algorithms and by examining a variety of applications. The book offers topical sections on sequence clustering and learning with Markov models, sequence prediction and recognition with neural networks, sequence discovery with symbolic methods, sequential decision making, biologically inspired sequence learning models.