Advances in Artificial Intelligence: 23rd Canadian by Atefeh Farzindar, Vlado Keselj

By Atefeh Farzindar, Vlado Keselj

This quantity includes the papers awarded on the twenty third Canadian convention on Arti?cial Intelligence (AI 2010). The convention was once held in Ottawa, Ontario, fromMay31toJune2,2010, andwascollocatedwiththe36thGraphicsInterface Conference(GI2010), andthe7thCanadianConferenceonComputerandRobot imaginative and prescient (CRV 2010). this system Committee obtained ninety submissions for the most convention, AI2010, fromacrossCanadaandaroundtheworld.Eachsubmissionwasreviewed byuptofourreviewers.Forthe?nalconferenceprogramandforinclusioninthese lawsuits, 22 general papers, with allocation of 12 pages each one, have been chosen. Additionally,26 shortpapers, with allocationof four pageseach, wereaccepted. The papers from the Graduate scholar Symposium also are incorporated within the complaints: sixoral(fourpages)andsixposter(twopages)presentationpapers. The convention programfeatured 3 keynote displays via Dekang Lin (Google Inc.), man Lapalme (UniversitedeMontr eal), and Evangelos Milios (Dalhousie University). The one-page abstracts in their talks also are incorporated within the complaints. pre-conference workshops, every one with their very own complaints, have been hung on could 30, 2010. The Workshop on clever equipment for safeguarding privateness and Con?dentiality in facts was once equipped by way of Khaled El Emam and Marina Sokolova. The workshop on educating AI in Computing and knowledge Te- nology (AI-CIT 2010) used to be prepared by means of Danny Silver, Leila Kosseim, and Sajid Hussain. This convention wouldnot havebeen attainable with out the hardworkofmany people.WewouldliketothankallProgramCommitteemembersandexternal- audience for his or her e?ort in delivering top quality reports in a well timed demeanour. We thank all of the authors of submitted papers for filing their paintings, and the - thors of chosen papers for his or her collaboration in instruction of the ?nal reproduction. ManythankstoEbrahimBagheriandMarinaSokolovafororganizingtheGra- ateStudentSymposium, andchairingtheProgramCommitteeofthesymposium. we're in debt to Andrei Voronkov for constructing the EasyChair convention managementsystemandmakingitfreelyavailabletotheacademicworld.Itisan amazinglyelegantand functionalWeb-basedsystem, whichsavedus muchtime.

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Extra info for Advances in Artificial Intelligence: 23rd Canadian Conference on Artificial Intelligence, Canadian AI 2010, Ottawa, Canada, May 31 - June 2, 2010, Proceedings (Lecture Notes in Computer Science)

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North-Holland, Amsterdam (1990) 17. : Decision-theoretic rough set models. ´ ezak, D. ) RSKT 2007. , Sl¸ vol. 4481, pp. 1–12. Springer, Heidelberg (2007) 18. : An email classification scheme based on decision-theoretic rough set theory and analysis of email security. In: Proceeding of 2005 IEEE Region 10 TENCON, pp. 1–6 (2005) 19. : Variable precision rough sets model. ca Abstract. We explore the task of automatic classification of texts by the emotions expressed. We consider how the presence of neutral instances affects the performance of distinguishing between emotions.

Our experiments on a model for supervised learning, using the previously generated data, are described and discussed in Section 4. We conclude by introducing new perspectives. 2 Producing the Summary of a Legal Decision Decisions are available on the Canada courts’ websites in HTML1 . which are analyzed in DecisionExpressTM to produce an analytic sheet for each decision containing information extracted from the decision such as the decision’s headline and conclusion, the judge’s name, the court level and the addressed topics.

Since P r(C|x) + P r(C c |x) = 1, we can simplify the rules based only on the probabilities P r(C|x) and the loss function λ. Consider a special kind of loss functions with: (c0). λPP ≤ λBP < λNP , λNN ≤ λBN < λPN . (9) That is, the loss of classifying an email x being in C into the positive region POS(C) is less than or equal to the loss of classifying x into the boundary region BND(C), and both of these losses are strictly less than the loss of classifying x into the negative region NEG(C). The reverse order of losses is used for classifying an email not in C.

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