Muutke küpsiste eelistusi

Agents and Data Mining Interaction: 4th International Workshop on Agents and Data Mining Interaction, ADMI 2009, Budapest, Hungary, May 10-15,2009, Revised Selected Papers 2009 ed. [Pehme köide]

Edited by , Edited by , Edited by , Edited by , Edited by
  • Formaat: Paperback / softback, 199 pages, kõrgus x laius: 235x155 mm, kaal: 454 g, XII, 199 p., 1 Paperback / softback
  • Sari: Lecture Notes in Computer Science 5680
  • Ilmumisaeg: 04-Aug-2009
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3642036023
  • ISBN-13: 9783642036026
Teised raamatud teemal:
  • Pehme köide
  • Hind: 48,70 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Tavahind: 57,29 €
  • Säästad 15%
  • Raamatu kohalejõudmiseks kirjastusest kulub orienteeruvalt 2-4 nädalat
  • Kogus:
  • Lisa ostukorvi
  • Tasuta tarne
  • Tellimisaeg 2-4 nädalat
  • Lisa soovinimekirja
  • Formaat: Paperback / softback, 199 pages, kõrgus x laius: 235x155 mm, kaal: 454 g, XII, 199 p., 1 Paperback / softback
  • Sari: Lecture Notes in Computer Science 5680
  • Ilmumisaeg: 04-Aug-2009
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3642036023
  • ISBN-13: 9783642036026
Teised raamatud teemal:
The2009InternationalWorkshoponAgentsandDataMiningInteraction(ADMI 2009) was a joint event with AAMAS2009. In recentyears,agents and data mining interaction (ADMI), or agent mining forshort,hasemergedasaverypromisingresearch eld. Followingthesuccessof ADMI 2006 in Hong Kong, ADMI 2007 in San Jose, and ADMI 2008 in Sydney, the ADMI 2009 workshop in Budapest provided a premier forum for sharing research and engineering results, as well as potential challenges and prospects encountered in the synergy between agents and data mining. As usual, the ADMI workshop encouraged and promoted theoretical and applied research and development, which aims at: – Exploitingagent-drivendatamininganddemonstratinghowintelligentagent technology can contribute to critical data mining problems in theory and practice – Improving data mining-driven agents and showing how data mining can strengthen agent intelligence in research and practical applications – Exploring the integration of agents and data mining toward a super-intelligent information processing and systems – Identifying challenges and directions for future research on the synergy between agents and data mining ADMI 2009 featured two invited talks and twelve selected papers. The ?rst invited talk was on “Agents and Data Mining in Bioinformatics,” with the s- ond focusing on “Knowledge-Based Reinforcement Learning. ” The ten accepted papers are from seven countries. A majority of submissions came from Eu- pean countries, indicating the boom of ADMI research in Europe. In addition the two invited papers, addressed fundamental issues related to agent-driven data mining, data mining-driven agents, and agent mining applications. The proceedings of the ADMI workshops will be published as part of the LNAIseriesbySpringer. WeappreciatethesupportofSpringer,andinparticular Alfred Hofmann.
Invited Talks and Papers.- Agents and Data Mining in Bioinformatics:
Joining Data Gathering and Automatic Annotation with Classification and
Distributed Clustering.- Knowledge-Based Reinforcement Learning for Data
Mining.- Ubiquitous Intelligence in Agent Mining.- Agents Based Data Mining
and Decision Support System.- Agent-Driven Data Mining.- Agent-Enriched Data
Mining Using an Extendable Framework.- Auto-Clustering Using Particle Swarm
Optimization and Bacterial Foraging.- A Self-Organized Multiagent System for
Intrusion Detection.- Towards Cooperative Predictive Data Mining in
Competitive Environments.- Data Mining Driven Agents.- Improving Agent
Bidding in Power Stock Markets through a Data Mining Enhanced Agent
Platform.- Enhancing Agent Intelligence through Data Mining: A Power Plant
Case Study.- A Sequence Mining Method to Predict the Bidding Strategy of
Trading Agents.- Agent Mining Applications.- Agent Assignment for Process
Management: Pattern Based Agent Performance Evaluation.- Concept Learning for
Achieving Personalized Ontologies: An Active Learning Approach.- The Complex
Dynamics of Sponsored Search Markets.