
By Zareen Syed, Tim Finin (auth.), Joaquim Filipe, Ana Fred, Bernadette Sharp (eds.)
ISBN-10: 3642198899
ISBN-13: 9783642198892
This ebook constitutes the completely refereed post-conference lawsuits of the second one overseas convention on brokers and synthetic Intelligence, ICAART 2010, held in Valencia, Spain, in January 2010. The 17 revised complete papers offered including an invited paper have been conscientiously reviewed and chosen from 364 submissions. related because the convention the papers are equipped in simultaneous tracks: man made Intelligence and brokers. the chosen papers mirror the interdisciplinary nature of the convention. the variety of subject matters is a crucial characteristic of this convention, allowing an total belief of a number of vital clinical and technological trends.
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Additional info for Agents and Artificial Intelligence: Second International Conference, ICAART 2010, Valencia, Spain, January 22-24, 2010. Revised Selected Papers
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ACM, New York (2007) 13. : Wikipedia as an ontology for describing documents. In: Proceedings of the Second International Conference on Weblogs and Social Media. AAAI Press, Menlo Park (2008) 14. : Using wikitology for crossdocument entity coreference resolution. In: Proceedings of the AAAI Spring Symposium on Learning by Reading and Learning to Read. AAAI Press, Menlo Park (2009) 15. : Application of spreading activation techniques in information retrieval. Artificial Intelligence Review 11(6), 453–482 (1997) 16.
To receive vertical lines we apply the Contour Following Algorithm [11]. Compared to other transformations, this method has the advantage that we obtain the starting and end points of these lines. 42 J. Hensler, M. Blaich, and O. Bittel Fig. 3. This Image illustrates our approach to find doors. Several weak classifiers are combined using the AdaBoost algorithm in the training process. The door width and door concavity classifiers are using the laser distance data from the robot in addition to the camera image.
For instance, XDM encodes XPath expressions into XML attributes to select sources for the mining, while KDDML uses an XQuery expression to evaluate a condition on a mining model. In our opinion, XQuake offers a deeper amalgamation with the XQuery language and consequently a better integration between DM and XML native databases. Finally, the XMineRule operator [11] defines the basic concept of association rules for XML documents. Two are the main differences with respect to XQuake. From the Language Support to XML Data Mining: A Case Study 37 physical point of view, XMineRule requires that the data are mapped to the relational model and it uses SQL-oriented algorithms to do the mining.
Agents and Artificial Intelligence: Second International Conference, ICAART 2010, Valencia, Spain, January 22-24, 2010. Revised Selected Papers by Zareen Syed, Tim Finin (auth.), Joaquim Filipe, Ana Fred, Bernadette Sharp (eds.)
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