By Marc Sebban, Gilles Richard (auth.), David J. Hand, Joost N. Kok, Michael R. Berthold (eds.)
This ebook constitutes the refereed court cases of the 3rd overseas Symposium on clever information research, IDA-99 held in Amsterdam, The Netherlands in August 1999.
The 21 revised complete papers and 23 posters awarded within the e-book have been conscientiously reviewed and chosen from a complete of greater than a hundred submissions. The papers handle all present facets of clever information research; they're geared up in sections on studying, visualization, type and clustering, integration, purposes and media mining.
Read or Download Advances in Intelligent Data Analysis: Third International Symposium, IDA-99 Amsterdam, The Netherlands, August 9–11, 1999 Proceedings PDF
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Extra info for Advances in Intelligent Data Analysis: Third International Symposium, IDA-99 Amsterdam, The Netherlands, August 9–11, 1999 Proceedings
Stone. Classification and Regression Trees. Wadsworth, 1984. C. Blake, E. J. Merz. UCI repository of machine learning databases. 1998. html. W. Buntine and T. Niblett. A further comparison of splitting rules for DecisionTree induction. Machine Learning, pages 75–85, 1992. P. Clark and T. Niblett. The CN2 induction algorithm. Machine Learning, 3:261– 283, 1989. P. Domingos. A Process-oriented Heuristic for Model selection. In Proc. of the 15 th ICML, pages 127–135, 1998. E. Franck and I. Witten.
T. their general convergence results on DT. Its principle is the same : “can we compare, when testing some rule (ti , v i ) and using the examples that satisfy the rule, the errors before and after removing the rule”? Name (ti ,vi ) as the error before removing the rule, on the local sample LS(ti ,vi ) satisfying monomial ti . Denote ∅ as the error before removing (ti , v i ), still measured on the local sample LS(ti ,vi ) . Then we define the “penalty” α(ti ,vi ) = (Set(v) + 2) log(n) + 2 log 1/δ |LS(ti ,vi ) | 34 Richard Nock and Pascal Jappy Set(v) denotes the total number of literal of all other rules in the DC that an arbitrary example could satisfy.
An ALM process has been introduced as well, and its behaviour has been compared to other Linguistic 26 Oscar Cord´ on and Francisco Herrera Modelling techniques in solving two different problems. The proposed process has obtained very good results. 3, our process has the capability of distinguishing the unnecesary rules and of generating KBs with good cooperation. The ALM operation mode based on: a) generating a preliminary fuzzy rule set with a large number of rules —considering double-consequent ones if it is necessary— and b) selecting the subset of them cooperating best allows us to obtain good results in the area of Linguistic Modelling.
Advances in Intelligent Data Analysis: Third International Symposium, IDA-99 Amsterdam, The Netherlands, August 9–11, 1999 Proceedings by Marc Sebban, Gilles Richard (auth.), David J. Hand, Joost N. Kok, Michael R. Berthold (eds.)