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Titlebook: Agents and Data Mining Interaction; 4th International Wo Longbing Cao,Vladimir Gorodetsky,Philip S. Yu Conference proceedings 2009 Springer

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31#
發(fā)表于 2025-3-26 23:09:40 | 只看該作者
32#
發(fā)表于 2025-3-27 01:20:40 | 只看該作者
https://doi.org/10.1057/9781137013125ch an ontology is unrealistic and its maintenance is cumbersome. Burden of maintaining a common ontology can be alleviated by enabling agents to evolve their ontologies personally. However, with different ontologies, agents are likely to run into communication problems since their vocabularies are d
33#
發(fā)表于 2025-3-27 07:03:35 | 只看該作者
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發(fā)表于 2025-3-27 12:15:38 | 只看該作者
Agents and Data Mining Interaction978-3-642-03603-3Series ISSN 0302-9743 Series E-ISSN 1611-3349
35#
發(fā)表于 2025-3-27 15:13:17 | 只看該作者
https://doi.org/10.1007/978-3-642-03603-3agent architectures; agent assignment; agent interaction; agent systems implementation; agent technology
36#
發(fā)表于 2025-3-27 21:11:56 | 只看該作者
37#
發(fā)表于 2025-3-27 22:37:48 | 只看該作者
38#
發(fā)表于 2025-3-28 02:37:16 | 只看該作者
Jason G. Irizarry,John W. RaibleMultiagent systems and data mining techniques are being frequently used in genome projects, especially regarding the annotation process (annotation pipeline). This paper discusses annotation-related problems where agent-based and/or distributed data mining has been successfully employed.
39#
發(fā)表于 2025-3-28 08:14:51 | 只看該作者
40#
發(fā)表于 2025-3-28 12:29:34 | 只看該作者
Knowledge-Based Reinforcement Learning for Data Mininge distinguished. The first approach is concerned with mining an agent’s observation data in order to extract patterns, categorize environment states, and/or make predictions of future states. In this setting, data is normally available as a batch, and the agent’s actions and goals are often independ
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