9786138921714 - Long life learning system for document understanding: Document understanding in cognitive manner di Tomovic, Savo; Pavlovic, Kosta (9 risultati)

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Paperback. Condizione: Brand New. 76 pages. 8.66x5.91x0.18 inches. In Stock.

Taschenbuch. Condizione: Neu. Neuware -We present long life learning (LLL) system for understanding and processing administrative documents. Special attention was devoted to document classification and information extraction. These two modules represent the most significant part of LLL document understanding system. When changes occur in the document layout or when a novel class of documents appears, the system can adapt to these modifications by running auto-learning procedure. The system does not require a large training data set for creating the initial knowledge. Under specific conditions, it is possible to run the system without preliminary model training. The system will start without knowledge and continuously build and adapt necessary models with each document being processed from the input stream. Platform can process and effectively incorporate feedback from the user into already accumulated knowledge. The proposed solution is comparable to the concurrent systems known from the literature and in some respects even more innovative and appropriate to use in practice. Of course, to achieve accuracy close to a human user much more time, resources and common efforts of all dedicated research groups is needed.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 76 pp. Englisch.

Taschenbuch. Condizione: Neu. Long life learning system for document understanding | Document understanding in cognitive manner | Savo Tomovic (u. a.) | Taschenbuch | Englisch | 2020 | Scholars' Press | EAN 9786138921714 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -We present long life learning (LLL) system for understanding and processing administrative documents. Special attention was devoted to document classification and information extraction. These two modules represent the most significant part of LLL document understanding system. When changes occur in the document layout or when a novel class of documents appears, the system can adapt to these modifications by running auto-learning procedure. The system does not require a large training data set for creating the initial knowledge. Under specific conditions, it is possible to run the system without preliminary model training. The system will start without knowledge and continuously build and adapt necessary models with each document being processed from the input stream. Platform can process and effectively incorporate feedback from the user into already accumulated knowledge. The proposed solution is comparable to the concurrent systems known from the literature and in some respects even more innovative and appropriate to use in practice. Of course, to achieve accuracy close to a human user much more time, resources and common efforts of all dedicated research groups is needed. 76 pp. Englisch.

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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Tomovic SavoSavo Tomovic received his PhD in computer science from the University of Montenegro. He is currently an associated professor in the Faculty of Science - Department of Mathematics and Computer Science at University of Mont.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - We present long life learning (LLL) system for understanding and processing administrative documents. Special attention was devoted to document classification and information extraction. These two modules represent the most significant part of LLL document understanding system. When changes occur in the document layout or when a novel class of documents appears, the system can adapt to these modifications by running auto-learning procedure. The system does not require a large training data set for creating the initial knowledge. Under specific conditions, it is possible to run the system without preliminary model training. The system will start without knowledge and continuously build and adapt necessary models with each document being processed from the input stream. Platform can process and effectively incorporate feedback from the user into already accumulated knowledge. The proposed solution is comparable to the concurrent systems known from the literature and in some respects even more innovative and appropriate to use in practice. Of course, to achieve accuracy close to a human user much more time, resources and common efforts of all dedicated research groups is needed.