Scopul nostru este sprijinirea şi promovarea cercetării ştiinţifice şi facilitarea comunicării între cercetătorii români din întreaga lume.
Autori: Razvan Andonie; Lucian Mircea Sasu; Angel Cataron
Editorial: International Journal of Computers, Communications and Control, IV, p.104-117, 2009.
Fuzzy ARTMAP with Relevance factor (FAMR) is a Fuzzy ARTMAP
(FAM) neural architecture with the following property: Each training pair has a relevance
factor assigned to it, proportional to the importance of that pair during the
learning phase. Using a relevance factor adds more flexibility to the training phase,
allowing ranking of sample pairs according to the confidence we have in the information
source or in the pattern itself.
We introduce a novel FAMR architecture: FAMR with Feature Weighting (FAMRFW).
In the first stage, the training data features are weighted. In our experiments,
we use a feature weighting method based on Onicescu’s informational energy (IE). In
the second stage, the obtained weights are used to improve FAMRFW training. The
effect of this approach is that category dimensions in the direction of relevant features
are decreased, whereas category dimensions in the direction of non-relevant feature
are increased. Experimental results, performed on several benchmarks, show that
feature weighting can improve the classification performance of the general FAMR
Cuvinte cheie: Fuzzy ARTMAP, ponderarea trasaturilor, LVQ, energia informationala a lui Onicescu // Fuzzy ARTMAP, feature weighting, LVQ, Onicescu’s informational energy