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.
"Sinopsis" puede pertenecer a otra edición de este libro.
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.
"Sobre este título" puede pertenecer a otra edición de este libro.
Librería: Brook Bookstore On Demand, Napoli, NA, Italia
Condición: new. Questo è un articolo print on demand. Nº de ref. del artículo: ca4d27599ad0dee19f6eef67199cbac8
Cantidad disponible: Más de 20 disponibles
Librería: Ria Christie Collections, Uxbridge, Reino Unido
Condición: New. In. Nº de ref. del artículo: ria9783642269721_new
Cantidad disponible: Más de 20 disponibles
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Taschenbuch. 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: 9783642269721
Cantidad disponible: 2 disponibles
Librería: moluna, Greven, Alemania
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: 5055012
Cantidad disponible: Más de 20 disponibles
Librería: Revaluation Books, Exeter, Reino Unido
Paperback. Condición: Brand New. 2012 edition. 200 pages. 9.25x6.10x0.46 inches. In Stock. Nº de ref. del artículo: x-3642269729
Cantidad disponible: 2 disponibles
Librería: preigu, Osnabrück, Alemania
Taschenbuch. Condición: Neu. Detection and Identification of Rare Audio-visual Cues | Daphna Weinshall (u. a.) | Taschenbuch | viii | Englisch | 2013 | Springer | EAN 9783642269721 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. Nº de ref. del artículo: 105538150
Cantidad disponible: 5 disponibles
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. 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: 9783642269721
Cantidad disponible: 1 disponibles
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Taschenbuch. 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: 9783642269721
Cantidad disponible: 1 disponibles
Librería: Mispah books, Redhill, SURRE, Reino Unido
Paperback. Condición: Like New. Like New. book. Nº de ref. del artículo: ERICA78736422697296
Cantidad disponible: 1 disponibles