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Sinopsis
Machine learning builds models of the world using training data from the application domain and prior knowledge about the problem. The models are later applied to future data in order to estimate the current state of the world. An implied assumption is that the future is stochastically similar to the past. The approach fails when the system encounters situations that are not anticipated from the past experience. In contrast, successful natural organisms identify new unanticipated stimuli and situations and frequently generate appropriate responses.
The observation described above lead to the initiation of the DIRAC EC project in 2006. In 2010 a workshop was held, aimed to bring together researchers and students from different disciplines in order to present and discuss new approaches for identifying and reacting to unexpected events in information-rich environments. This book includes a summary of the achievements of the DIRAC project in chapter 1, and a collection of the papers presented in this workshop in the remaining parts.
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De la contraportada
Machine learning builds models of the world using training data from the application domain and prior knowledge about the problem. The models are later applied to future data in order to estimate the current state of the world. An implied assumption is that the future is stochastically similar to the past. The approach fails when the system encounters situations that are not anticipated from the past experience. In contrast, successful natural organisms identify new unanticipated stimuli and situations and frequently generate appropriate responses.
The observation described above lead to the initiation of the DIRAC EC project in 2006. In 2010 a workshop was held, aimed to bring together researchers and students from different disciplines in order to present and discuss new approaches for identifying and reacting to unexpected events in information-rich environments. This book includes a summary of the achievements of the DIRAC project in chapter 1, and a collection of the papers presented in this workshop in the remaining parts.
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- EditorialSpringer
- Año de publicación2011
- ISBN 10 364224033X
- ISBN 13 9783642240331
- EncuadernaciónTapa dura
- IdiomaInglés
- Número de páginas200
- EditorWeinshall Daphna, Anem Ller J. Rn, Van Gool Luc
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Detection and Identification of Rare Audio-visual Cues
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Springer Berlin Heidelberg, 2011
ISBN 10: 364224033X
ISBN 13: 9783642240331
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Detection and Identification of Rare Audio-visual Cues
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Springer Berlin Heidelberg, 2011
ISBN 10: 364224033X
ISBN 13: 9783642240331
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Recent research in Detection and Identification of Rare Audiovisual Cues Scientific outcome of the European project DIRAC (Detection and Identification of Rare Audio-visual Cues) Written by leading experts in the fieldMachine . Nº de ref. del artículo: 5053415
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Detection and Identification of Rare Audiovisual Cues
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Detection and Identification of Rare Audio-visual Cues
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Springer Berlin Heidelberg, Springer Berlin Heidelberg Okt 2011, 2011
ISBN 10: 364224033X
ISBN 13: 9783642240331
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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Machine learning builds models of the world using training data from the application domain and prior knowledge about the problem. The models are later applied to future data in order to estimate the current state of the world. An implied assumption is that the future is stochastically similar to the past. The approach fails when the system encounters situations that are not anticipated from the past experience. In contrast, successful natural organisms identify new unanticipated stimuli and situations and frequently generate appropriate responses. The observation described above lead to the initiation of the DIRAC EC project in 2006. In 2010 a workshop was held, aimed to bring together researchers and students from different disciplines in order to present and discuss new approaches for identifying and reacting to unexpected events in information-rich environments. This book includes a summary of the achievements of the DIRAC project in chapter 1, and a collection of the papers presented in this workshop in the remaining parts. 200 pp. Englisch. Nº de ref. del artículo: 9783642240331
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Detection and Identification of Rare Audio-visual Cues
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Springer Berlin Heidelberg, 2011
ISBN 10: 364224033X
ISBN 13: 9783642240331
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Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - Machine learning builds models of the world using training data from the application domain and prior knowledge about the problem. The models are later applied to future data in order to estimate the current state of the world. An implied assumption is that the future is stochastically similar to the past. The approach fails when the system encounters situations that are not anticipated from the past experience. In contrast, successful natural organisms identify new unanticipated stimuli and situations and frequently generate appropriate responses. The observation described above lead to the initiation of the DIRAC EC project in 2006. In 2010 a workshop was held, aimed to bring together researchers and students from different disciplines in order to present and discuss new approaches for identifying and reacting to unexpected events in information-rich environments. This book includes a summary of the achievements of the DIRAC project in chapter 1, and a collection of the papers presented in this workshop in the remaining parts. Nº de ref. del artículo: 9783642240331
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Detection and Identification of Rare Audiovisual Cues
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Detection and Identification of Rare Audiovisual Cues
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Detection and Identification of Rare Audio-visual Cues
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Springer Berlin Heidelberg, Springer Berlin Heidelberg Okt 2011, 2011
ISBN 10: 364224033X
ISBN 13: 9783642240331
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
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Buch. Condición: Neu. Neuware -Machine learning builds models of the world using training data from the application domain and prior knowledge about the problem. The models are later applied to future data in order to estimate the current state of the world. An implied assumption is that the future is stochastically similar to the past. The approach fails when the system encounters situations that are not anticipated from the past experience. In contrast, successful natural organisms identify new unanticipated stimuli and situations and frequently generate appropriate responses.The observation described above lead to the initiation of the DIRAC EC project in 2006. In 2010 a workshop was held, aimed to bring together researchers and students from different disciplines in order to present and discuss new approaches for identifying and reacting to unexpected events in information-rich environments. This book includes a summary of the achievements of the DIRAC project in chapter 1, and a collection of the papers presented in this workshop in the remaining parts.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 200 pp. Englisch. Nº de ref. del artículo: 9783642240331
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Detection and Identification of Rare Audio-visual Cues (Studies in Computational Intelligence, 384)
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Detection and Identification of Rare Audiovisual Cues
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Condición: As New. Unread book in perfect condition. Nº de ref. del artículo: 13858340
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