By Francisco Bueno (auth.), Pavel Brazdil, Alípio Jorge (eds.)

The 10th Portuguese convention on Arti?cial Intelligence, EPIA 2001 was once held in Porto and persevered the culture of past meetings within the sequence. It lower back to the town during which the ?rst convention happened, approximately 15 years in the past. The convention used to be geared up, as ordinary, lower than the auspices of the Portuguese organization for Arti?cial Intelligence (APPIA, http://www.appia.pt). EPIA maintained its overseas personality and persisted to supply a discussion board for p- senting and discussing researc h on di?erent points of Arti?cial Intelligence. to advertise inspired discussions between members, this convention streng- ened the function of the thematic workshops. those weren't simply satellite tv for pc occasions, yet relatively shaped a vital part of the convention, with joint periods while justi?ed. This had the virtue that the paintings was once offered to a stimulated viewers. This used to be the ?rst time that EPIA launched into this adventure and so supplied us with extra challenges.

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0 11 mains where classification is very sensitive to the parameter k by using the k-NN algorithm. For these input data, we could summarize several aspects: – Without the need of parameter, fNN is a reduction and classification technique that keeps the average accuracy of the k-NN algorithm. – kLim and the size of Tf compared to the size of T are an approximated indicator for the percentage of examples that cannot be correctly classified by the k-NN algorithm. – The reduction of the database is very similar to the reduction that makes CNN [15], so that fNN is less restrictive than CNN.

Each particular sense of the word is related to a specific type of distribution. Given that the clustering methods based on the distributional hypothesis solely take into account the global distribution of a word, they are not able to separate and acquire its different contextual senses. In order to extract contextual word classes from the appropriate syntactic constructions, we claim that similar syntactic contexts share the same semantic restrictions on words. Instead of computing word similarity on the basis of the too coarse-grained distributional hypothesis, we measure the similarity between syntactic contexts in order to identify common selection restrictions.

Keller, I. Paterson, and H. Berrer. An integrated concept for multi-criteria ranking of data-mining algorithms. In J. Keller and C. Giraud-Carrier, editors, MetaLearning: Building Automatic Advice Strategies for Model Selection and Method Combination, 2000. 5. R. Kohavi, G. John, R. Long, D. Mangley, and K. Pfleger. MLC++: A machine learning library in c++. Technical report, Stanford University, 1994. 6. D. J. C. Taylor. Machine Learning, Neural and Statistical Classification. Ellis Horwood, 1994.

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