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Kategorie szczegółowe BISAC

Detection and Identification of Rare Audio-visual Cues

ISBN-13: 9783642240331 / Angielski / Twarda / 2011 / 192 str.

Daphna Weinshall;Jörn Anemüller;Luc van Gool
Detection and Identification of Rare Audio-visual Cues Daphna Weinshall, Jörn Anemüller, Luc van Gool 9783642240331 Springer-Verlag Berlin and Heidelberg GmbH &  - książkaWidoczna okładka, to zdjęcie poglądowe, a rzeczywista szata graficzna może różnić się od prezentowanej.

Detection and Identification of Rare Audio-visual Cues

ISBN-13: 9783642240331 / Angielski / Twarda / 2011 / 192 str.

Daphna Weinshall;Jörn Anemüller;Luc van Gool
cena 401,58
(netto: 382,46 VAT:  5%)

Najniższa cena z 30 dni: 385,52
Termin realizacji zamówienia:
ok. 22 dni roboczych
Dostawa w 2026 r.

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inne wydania

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.

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.

Kategorie:
Informatyka, Bazy danych
Kategorie BISAC:
Technology & Engineering > Engineering (General)
Computers > Artificial Intelligence - General
Computers > Interactive & Multimedia
Wydawca:
Springer-Verlag Berlin and Heidelberg GmbH &
Seria wydawnicza:
Studies in Computational Intelligence
Język:
Angielski
ISBN-13:
9783642240331
Rok wydania:
2011
Dostępne języki:
Angielski
Wydanie:
2012
Numer serii:
000318395
Ilość stron:
192
Waga:
0.51 kg
Wymiary:
23.523.5 x 15.5
Oprawa:
Twarda
Wolumenów:
01

Introduction.- The DIRAC project.- The detection of incongruent events, project survey and algorithms.- Alternative frameworks to detect meaningful novel events.- Dealing with meaningful novel events, what to do after detection.- How biological systems deal with novel and incongruent events.

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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