Ranilla, J., & Bahamonde, A.: FAN: Finding Accurate iNductions. Technical Report, Artificial Intelligence Center, University of Oviedo at Gijn. November (1998) [ftp://ftp.aic.uniovi.es/publications/Machine_Learning/Fanprn.ZIP] Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
Autonomous Clustering for Machine Learning - Oscar Luaces Juan (1999) Self-citation (Ranilla Bahamonde) (Correct)
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Ranilla, J., & Bahamonde, A.: FAN: Finding Accurate iNductions. Technical Report, Artificial Intelligence Center, University of Oviedo at Gijn. November (1998) [ftp://ftp.aic.uniovi.es/publications/Machine_Learning/Fanprn.ZIP]
Inflating Examples to Obtain Rules - Luaces, Bahamonde (2002) Self-citation (Bahamonde) (Correct)
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Jose Ranilla and Antonio Bahamonde. Fan: Finding Accurate iNductions. International Journal of Human Computer Studies, 2002. In press.
A Heuristic for Learning Decision Trees and Pruning.. - Ranilla, Luaces.. Self-citation (Ranilla Bahamonde) (Correct)
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J. Ranilla and A. Bahamonde. Fan: Finding Accurate iNductions. International Journal of Human Computer Studies, 56(4):445--474, June 2002.
Inflating Examples to Obtain Rules - Luaces, Bahamonde (2002) Self-citation (Bahamonde) (Correct)
No context found.
Jos Ranilla and Antonio Bahamonde. Fan: Finding Accurate iNductions. International Journal of Human Computer Studies, 2002. In press.
A Heuristic for Learning Decision Trees and Pruning.. - Ranilla, Luaces.. Self-citation (Ranilla Bahamonde) (Correct)
No context found.
J. Ranilla and A. Bahamonde. Fan: Finding Accurate iNductions. International Journal of Human Computer Studies, 56(4):445-474, June 2002.
Self-Organizing Cases to Find Paradigms - Coz, Luaces, Quevedo, Alonso.. (1999) (2 citations) Self-citation (Ranilla Bahamonde) (Correct)
....as models for future classifications; in other words, we conclude with a set of rules whose conditions are part of some well chosen training examples. The evaluation procedure of these rules will follow a minimum distance criterion, as in a nearest neighbor (NN) learning algorithm [5] 1] 13] [14], 15] The solutions available in the literature range from the NN algorithm, which stores all the training set, to Aha s IBx family [1] where a serious effort is made to reduce the number of stored instances. However, the number of retained cases is of the same order as the initial training ....
....we compute the coverage of labeled nodes of the map. In order to weight the promising classification quality of the original examples gathered in a node, we use a function called impurity level. This is a measure inspired by Aha s IB3 [1] and was developed and very successfully used in FAN [13] [14], a machine learning system that produces classification rules. In this context, an initial selection takes place: we separate the nodes with best impurity levels in their coverage until a previously fixed proportion of the original data is involved. Now we try to discover the attributes that are ....
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Ranilla, J., & Bahamonde, A.: FAN: Finding Accurate iNductions. Technical Report, Artificial Intelligence Center, University of Oviedo at Gijn. November (1998) [ftp://ftp.aic.uniovi.es/publications/Machine_Learning/FANprn.ZIP]
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