High-performance knowledge extraction from data on PC-based networks of workstations
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 1586, Page: 1131-1144
1999
- 1Citations
- 4Captures
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Conference Paper Description
The automatic construction of classifiers (programs able to correctly classify data collected from the real world) is one of the major problems in pattern recognition and in a wide area related to Artificial Intelligence, including Data Mining. In this paper we present G-Net, a distributed algorithm able to infer classifiers from pre-collected data, and its implementation on PC-based Networks of Workstations (PC-NOWs). In order to effectively exploit the computing power provided by PCNOWs, G-Net incorporates a set of dynamic load distribution techniques that allow it to adapt its behavior to variations in the computing power due to resource contention. Moreover, it is provided with a fault tolerance scheme that enables it to continue its computation even if the majority of the machines become unavailable during its execution.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84957687209&origin=inward; http://dx.doi.org/10.1007/bfb0097998; http://link.springer.com/10.1007/BFb0097998; http://link.springer.com/content/pdf/10.1007/BFb0097998; https://dx.doi.org/10.1007/bfb0097998; https://link.springer.com/chapter/10.1007/BFb0097998
Springer Nature
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