9783845406985 - Active Learning for Image Recognition and Retrieval: Incremental Nonparametric Discriminant Analysis based Active Learning inspired by Cognitive Psychology di Dhobl... (5 risultati)

Taschenbuch. Condizione: Neu. Active Learning for Image Recognition and Retrieval | Incremental Nonparametric Discriminant Analysis based Active Learning inspired by Cognitive Psychology | Kshitij Dhoble | Taschenbuch | 128 S. | Englisch | 2012 | LAP LAMBERT Academic Publishing | EAN 9783845406985 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Learning is one such innate general cognitive ability which has empowered the living animate entities and especially humans with intelligence. It is obtained by acquiring new knowledge and skills that enable them to adapt and survive. With the advancement of technology, a large amount of information gets amassed. Due to the sheer volume of increasing information, its analysis is humanly unfeasible and impractical. Therefore, for the analysis of massive data we need machines (such as computers) with the ability to learn and evolve in order to discover new knowledge from the analysed data. The majority of the traditional machine learning algorithms function optimally on a parametric (static) data. However, the datasets acquired in real practices are often vast, inaccurate, inconsistent, non-parametric and highly volatile. Therefore, the learning algorithms optimized performance can only be transitory, thus requiring a learning algorithm that can constantly evolve and adapt according to the data it processes. In light of a need for such machine learning algorithm, we look for the inspiration in humans innate cognitive learning ability. Active learning is one such approach. 128 pp. Englisch.

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Dhoble KshitijHe has received his MCIS (Hons) degree in Computer and Information Sciences from Auckland University of Technology. He is currently a Ph.D. student. His research interests are in the areas of nature inspired neural netw.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Learning is one such innate general cognitive ability which has empowered the living animate entities and especially humans with intelligence. It is obtained by acquiring new knowledge and skills that enable them to adapt and survive. With the advancement of technology, a large amount of information gets amassed. Due to the sheer volume of increasing information, its analysis is humanly unfeasible and impractical. Therefore, for the analysis of massive data we need machines (such as computers) with the ability to learn and evolve in order to discover new knowledge from the analysed data. The majority of the traditional machine learning algorithms function optimally on a parametric (static) data. However, the datasets acquired in real practices are often vast, inaccurate, inconsistent, non-parametric and highly volatile. Therefore, the learning algorithms' optimized performance can only be transitory, thus requiring a learning algorithm that can constantly evolve and adapt according to the data it processes. In light of a need for such machine learning algorithm, we look for the inspiration in humans' innate cognitive learning ability. Active learning is one such approach.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 128 pp. Englisch.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Learning is one such innate general cognitive ability which has empowered the living animate entities and especially humans with intelligence. It is obtained by acquiring new knowledge and skills that enable them to adapt and survive. With the advancement of technology, a large amount of information gets amassed. Due to the sheer volume of increasing information, its analysis is humanly unfeasible and impractical. Therefore, for the analysis of massive data we need machines (such as computers) with the ability to learn and evolve in order to discover new knowledge from the analysed data. The majority of the traditional machine learning algorithms function optimally on a parametric (static) data. However, the datasets acquired in real practices are often vast, inaccurate, inconsistent, non-parametric and highly volatile. Therefore, the learning algorithms optimized performance can only be transitory, thus requiring a learning algorithm that can constantly evolve and adapt according to the data it processes. In light of a need for such machine learning algorithm, we look for the inspiration in humans innate cognitive learning ability. Active learning is one such approach.