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 … Mehr…
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. This volume brings together researchers from different disciplines to discuss new approaches for identifying, and reacting to, unexpected events in information-rich environments. eBook PDF 23.11.2011 eBooks>Fremdsprachige eBooks>Englische eBooks>Sach- & Fachthemen, Springer, .201<
eBooks, eBook Download (PDF), 2012, Machine learning builds models of the world using training data from the application domain and prior knowledge about the problem. The models are later… Mehr…
eBooks, eBook Download (PDF), 2012, 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. , [PU: Springer Berlin], Seiten: 192, Springer Berlin, 2011<
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 … Mehr…
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<
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This volume brings together researchers from different disciplines to discuss new approaches for identifying, and reacting to, unexpected events in information-rich environments. COMPUTER… Mehr…
This volume brings together researchers from different disciplines to discuss new approaches for identifying, and reacting to, unexpected events in information-rich environments. COMPUTERS,Intelligence (AI) & Semantics, eBooks.com<
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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 … Mehr…
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. This volume brings together researchers from different disciplines to discuss new approaches for identifying, and reacting to, unexpected events in information-rich environments. eBook PDF 23.11.2011 eBooks>Fremdsprachige eBooks>Englische eBooks>Sach- & Fachthemen, Springer, .201<
eBooks, eBook Download (PDF), 2012, Machine learning builds models of the world using training data from the application domain and prior knowledge about the problem. The models are later… Mehr…
eBooks, eBook Download (PDF), 2012, 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. , [PU: Springer Berlin], Seiten: 192, Springer Berlin, 2011<
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 … Mehr…
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<
Nr. 978-3-642-24034-8. Versandkosten:Worldwide free shipping, , DE. (EUR 0.00)
This volume brings together researchers from different disciplines to discuss new approaches for identifying, and reacting to, unexpected events in information-rich environments. COMPUTER… Mehr…
This volume brings together researchers from different disciplines to discuss new approaches for identifying, and reacting to, unexpected events in information-rich environments. COMPUTERS,Intelligence (AI) & Semantics, eBooks.com<
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Detailangaben zum Buch - Detection and Identification of Rare Audio-visual Cues
EAN (ISBN-13): 9783642240348 ISBN (ISBN-10): 3642240348 Erscheinungsjahr: 2011 Herausgeber: Springer Berlin 192 Seiten Sprache: eng/Englisch
Buch in der Datenbank seit 2012-10-24T21:09:35+02:00 (Vienna) Detailseite zuletzt geändert am 2024-01-24T20:06:06+01:00 (Vienna) ISBN/EAN: 9783642240348
ISBN - alternative Schreibweisen: 3-642-24034-8, 978-3-642-24034-8 Alternative Schreibweisen und verwandte Suchbegriffe: Autor des Buches: wein, anemüller, van gool Titel des Buches: visual, cues, identification
Daten vom Verlag:
Autor/in: Daphna Weinshall; Jörn Anemüller; Luc van Gool Titel: Studies in Computational Intelligence; Detection and Identification of Rare Audio-visual Cues Verlag: Springer; Springer Berlin 192 Seiten Erscheinungsjahr: 2011-11-23 Berlin; Heidelberg; DE Sprache: Englisch 96,29 € (DE) 99,00 € (AT) 118,00 CHF (CH) Available VIII, 192 p.
EA; E107; eBook; Nonbooks, PBS / Technik/Allgemeines, Lexika; Künstliche Intelligenz; Verstehen; Computational Intelligence; Detection of Rare Audiovisual Cues; Identification of Rare Audiovisual Cues; Machine Learning; B; Computational Intelligence; Artificial Intelligence; Multimedia Information Systems; Engineering; Grafische und digitale Media-Anwendungen; BC
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. 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 field
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