]> 2026-04-04T16:29:20+02:00 Publications:Evolving Committees of Support Vector Machines 500 en <p>The main emphasis of the technique developed in this work for evolving committees of support vector machines (SVM) is on a two phase procedure to select salient features. In the first phase, clearly redundant features are eliminated based on the paired t-test comparing the SVM output sensitivity-based saliency of the candidate and the noise feature. In the second phase, the genetic search integrating the steps of training, aggregation of committee members, and hyper-parameter as well as feature selection into the same learning process is employed. A small number of genetic iterations needed to find a solution is the characteristic feature of the genetic search procedure developed. The experimental tests performed on five real world problems have shown that significant improvements in correct classification rate can be obtained in a small number of iterations if compared to the case of using all the features available.</p> 5th International Conference on Machine Learning and Data Mining in Pattern Recognition, Leipzig, Germany, July 18-20, 2007 http://dx.doi.org/10.1007/978-3-540-73499-4_20 http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:239280 275 Machine Learning and Data Mining in Pattern Recognition, Proceedings Conference Paper Lecture Notes in Computer Science ; Volume 4571/2007 263 Evolving Committees of Support Vector Machines 2007 2016-09-30T20:40:41Z 2457662.3615856 Evolving Committees of Support Vector Machines 500 0 plainlist list 1 [[:Publications:Evolving Committees of Support Vector Machines]] Evolving Committees of Support Vector Machines# QUERY902b1b6849a4ae3faab5134da963109e 500 [2,"smw-datavalue-property-restricted-annotation-use","Publisher"] Evolving Committees of Support Vector Machines# ERR6c155b5d70b69ddb590b4f2e73389c2a 500 [2,"smw-datavalue-property-restricted-annotation-use","Author"] Evolving Committees of Support Vector Machines# ERRe24f17e090c98448f3de99d03182b7b6