Pattern Selection for Support Vector Classifiers
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
Maximum margin equalizers trained with the Adatron algorithm
Signal Processing
Invariance of neighborhood relation under input space to feature space mapping
Pattern Recognition Letters
Neighborhood Property--Based Pattern Selection for Support Vector Machines
Neural Computation
Selecting Samples and Features for SVM Based on Neighborhood Model
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Response modeling with support vector machines
Expert Systems with Applications: An International Journal
Designing Model Based Classifiers by Emphasizing Soft Targets
Fundamenta Informaticae - Advances in Artificial Intelligence and Applications
Fast pattern selection for support vector classifiers
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
A new formulation for classification by ellipsoids
TAINN'05 Proceedings of the 14th Turkish conference on Artificial Intelligence and Neural Networks
Designing Model Based Classifiers by Emphasizing Soft Targets
Fundamenta Informaticae - Advances in Artificial Intelligence and Applications
Fast instance selection for speeding up support vector machines
Knowledge-Based Systems
Benchmarking local classification methods
Computational Statistics
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Explores the possibility of constructing RBF classifiers which, somewhat like support vector machines, use a reduced number of samples as centroids, by means of selecting samples in a direct way. Because sample selection is viewed as a hard computational problem, this selection is done after a previous vector quantization: this way obtains also other similar machines using centroids selected from those that are learned in a supervised manner. Several forms of designing these machines are considered, in particular with respect to sample selection; as well as some different criteria to train them. Simulation results for well-known classification problems show very good performance of the corresponding designs, improving that of support vector machines and reducing substantially their number of units. This shows that our interest in selecting samples (or centroids) in an efficient manner is justified. Many new research avenues appear from these experiments and discussions, as suggested in our conclusions