NIYOGI, PARTHA (10 Ergebnisse)

Hardcover. Zustand: Very Good. No Jacket. Former library book; Missing dust jacket; May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.

Gr.8°, OLnbd. 1. Aufl. XVIII,482 S. English edition. Mit wissenschaftl. App. Stempel auf Titelblatt, schönes Ex. Sprache: Deutsch Gewicht in Gramm: 0.

Taschenbuch. Zustand: Neu. Neuware -Laminated composite plates and folded plates are very popular choice in aircraft and automobile industries due to their low specific weight (mass) and high specific strength and stiffness. These properties of the laminated composite structure help to reduce overall weight of the system without compromising its integrity. But, as these composite structures are very thin and lightly damped, they are highly susceptible to the external loading which might affect its gross performance. Thus, the need of vibration suppression of such types of structures is felt. An attempt is made in the present work to develop an active control algorithm to suppress the dynamic response of plate and folded plate structures.Books on Demand GmbH, Überseering 33, 22297 Hamburg 96 pp. Englisch.

Paperback. Zustand: Brand New. 96 pages. 8.66x5.91x0.22 inches. In Stock.

Zustand: New. In.

Zustand: New. In English.

Zustand: Sehr gut. 482 Seiten Zustand: Schutzumschlag etwas berieben und mit minimalen Randläsuren // Englische Ausgabe /// Versand gratis Innerhalb Deutschlands - Portofrei in Deutschland- ab 20 Euro mit Post ID - Gratisversand deutschlandweit innerhalb Deutschlands gratis Versand -Versandkostenfrei innerhalb Deutschlands /// Sprache: Englisch Gewicht in Gramm: 953 23,5 x 18,5 cm, Leinenband mit Schutzumschlag.

Buch. Zustand: Neu. Neuware -Among other topics, The Informational Complexity of Learning: Perspectives on Neural Networks and Generative Grammar brings together two important but very different learning problems within the same analytical framework. The first concerns the problem of learning functional mappings using neural networks, followed by learning natural language grammars in the principles and parameters tradition of Chomsky.These two learning problems are seemingly very different. Neural networks are real-valued, infinite-dimensional, continuous mappings. On the other hand, grammars are boolean-valued, finite-dimensional, discrete (symbolic) mappings. Furthermore the research communities that work in the two areas almost never overlap.The book's objective is to bridge this gap. It uses the formal techniques developed in statistical learning theory and theoretical computer science over the last decade to analyze both kinds of learning problems. By asking the same question - how much information does it take to learn - of both problems, it highlights their similarities and differences. Specific results include model selection in neural networks, active learning, language learning and evolutionary models of language change.The Informational Complexity of Learning: Perspectives on Neural Networks and Generative Grammar is a very interdisciplinary work. Anyone interested in the interaction of computer science and cognitive science should enjoy the book. Researchers in artificial intelligence, neural networks, linguistics, theoretical computer science, and statistics will find it particularly relevant.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 252 pp. Englisch.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Among other topics, The Informational Complexity of Learning: Perspectives on Neural Networks and Generative Grammar brings together two important but very different learning problems within the same analytical framework. The first concerns the problem of learning functional mappings using neural networks, followed by learning natural language grammars in the principles and parameters tradition of Chomsky. These two learning problems are seemingly very different. Neural networks are real-valued, infinite-dimensional, continuous mappings. On the other hand, grammars are boolean-valued, finite-dimensional, discrete (symbolic) mappings. Furthermore the research communities that work in the two areas almost never overlap. The book's objective is to bridge this gap. It uses the formal techniques developed in statistical learning theory and theoretical computer science over the last decade to analyze both kinds of learning problems. By asking the same question - how much information does it take to learn - of both problems, it highlights their similarities and differences. Specific results include model selection in neural networks, active learning, language learning and evolutionary models of language change. The Informational Complexity of Learning: Perspectives on Neural Networks and Generative Grammar is a very interdisciplinary work. Anyone interested in the interaction of computer science and cognitive science should enjoy the book. Researchers in artificial intelligence, neural networks, linguistics, theoretical computer science, and statistics will find it particularly relevant.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Among other topics, The Informational Complexity of Learning: Perspectives on Neural Networks and Generative Grammar brings together two important but very different learning problems within the same analytical framework. The first concerns the problem of learning functional mappings using neural networks, followed by learning natural language grammars in the principles and parameters tradition of Chomsky. These two learning problems are seemingly very different. Neural networks are real-valued, infinite-dimensional, continuous mappings. On the other hand, grammars are boolean-valued, finite-dimensional, discrete (symbolic) mappings. Furthermore the research communities that work in the two areas almost never overlap. The book's objective is to bridge this gap. It uses the formal techniques developed in statistical learning theory and theoretical computer science over the last decade to analyze both kinds of learning problems. By asking the same question - how much information does it take to learn - of both problems, it highlights their similarities and differences. Specific results include model selection in neural networks, active learning, language learning and evolutionary models of language change. The Informational Complexity of Learning: Perspectives on Neural Networks and Generative Grammar is a very interdisciplinary work. Anyone interested in the interaction of computer science and cognitive science should enjoy the book. Researchers in artificial intelligence, neural networks, linguistics, theoretical computer science, and statistics will find it particularly relevant.