Read e-book online Advances in Artificial Intelligence: 14th Biennial PDF

By Aijun An, Nick Cercone, Xiangji Huang (auth.), Eleni Stroulia, Stan Matwin (eds.)

ISBN-10: 3540421440

ISBN-13: 9783540421443

ISBN-10: 3540451536

ISBN-13: 9783540451532

AI 2001 is the 14th within the sequence of Arti cial Intelligence meetings backed by way of the Canadian Society for Computational stories of Intelligence/Soci et e - nadienne pour l’ etude de l’intelligence par ordinateur. As used to be the case final yr too, the convention is being held along side the once a year meetings of 2 different Canadian societies, images Interface (GI 2001) and imaginative and prescient Int- face (VI 2001). We think that the final event may be enriched by means of this conjunction of meetings. This 12 months is the \silver anniversary" of the convention: the rst Canadian AI convention was once held in 1976 at UBC. in the course of its lifetime, it has attracted Canadian and overseas papers of top of the range from a number of AI examine parts. All papers submitted to the convention acquired no less than 3 indep- dent studies. nearly one 3rd have been authorized for plenary presentation on the convention. the simplest paper of the convention should be invited to seem in Computational Intelligence.

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Read Online or Download Advances in Artificial Intelligence: 14th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI 2001 Ottawa, Canada, June 7–9, 2001 Proceedings PDF

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Additional resources for Advances in Artificial Intelligence: 14th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI 2001 Ottawa, Canada, June 7–9, 2001 Proceedings

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To obtain L1 , it first generates candidate set C1 which contains all 1-itemsets of basket data, then the database is scanned for each itemset in the set C1 to compute its support. The items with support greater than or equal to minimum support (minsupport) are chosen as large items L1 . The minsupport is provided by the user before mining. In the next iteration, apriori gen function [1] is used to generate candidate set C2 by joining L1 and L1 and keeping all unique itemsets with 2 items in each.

The effect on perplexity when each ranked term is added individually. The left-most points represent the highest ranked terms. H ( p , m) = - 1 log 2 m(t ) n t˛ L (6) where t is a term or a word in L. e. the lower H(p, m) will be). Hence, this model can serve to perform a comparative evaluation of different term extraction algorithms. 1 Analysis We used two corpora for evaluating the perplexity of our system: UNTS [12] consisting of 439,053 words and NAG [15] consisting of 117,582 words. We divided each corpus into 10 equal parts and performed ten-fold cross validation to test our system’s perplexity.

If any itemset in Lk is still large in the new database, its lower level subset itemsets are included to their appropriate level large itemsets in Lk−1 , Lk−2 , . . , L1 . For example, since L3 = {123} is confirmed to be still large in the new database, MAAP includes {12}, {13}, {23} to L2 and {1}, {2}, {3} to L1 . By so doing, some computation time is saved. 1 as the result in the old database, the MAAP algorithm proceeds by checking if each itemset in L3 = {ABC, ACD, BCD} is still large in the new database.

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Advances in Artificial Intelligence: 14th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI 2001 Ottawa, Canada, June 7–9, 2001 Proceedings by Aijun An, Nick Cercone, Xiangji Huang (auth.), Eleni Stroulia, Stan Matwin (eds.)


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