By Longbing Cao

ISBN-10: 3642551912

ISBN-13: 9783642551918

ISBN-10: 3642551920

ISBN-13: 9783642551925

This booklet constitutes the completely refereed and revised chosen papers from the ninth foreign Workshop on brokers and information Mining interplay, ADMI 2013, held in Saint Paul, MN, united states in could 2013. the ten papers awarded during this quantity have been conscientiously chosen for inclusion within the e-book and are equipped in topical sections named agent mining and information mining.

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Extra info for Agents and Data Mining Interaction: 9th International Workshop, ADMI 2013, Saint Paul, MN, USA, May 6-7, 2013, Revised Selected Papers

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Unsupervised personal name disambiguation. In: Proceedings of the 7th Conference on Natural Language Learning at HLT-NAACL 2003, vol. 4, pp. 33–40 (2003) 9. : Probabilistic author-topic models for information discovery. In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 306–315 (2005) 10.

Contributor vs. reviewer vs. module administrator)? Q3. Are there cultural differences within members of a project with regards to norms (inter- and intra- project comparisons) since individuals from different cultures may have different norms? Q4. Is there a difference between norm adoption and compliance between open-source and closed-source projects? 16 In this example only two artifacts, the email message and the log are involved. But in practice, several different types of documents may need to be traversed to find the relevant information.

References ˇ 1. : Argument based machine learning applied to law. Artif. Intell. Law 13(1), 53–73 (2005) 2. : Arguments and counterexamples in case-based joint deliberation. , Rahwan, I. ) ArgMAS 2006. LNCS (LNAI), vol. 4766, pp. 36–53. Springer, Heidelberg (2007) 3. : Towards the application of association rules for defeasible rules discovery. In: Jurix 2001, pp. 63–75 (2001) 4. : Integrating defeasible argumentation and machine learning techniques. arXiv preprint: cs/0402057 (2004) 5. : Multiagent inductive learning: an argumentation-based approach.

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Agents and Data Mining Interaction: 9th International Workshop, ADMI 2013, Saint Paul, MN, USA, May 6-7, 2013, Revised Selected Papers by Longbing Cao


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