We propose a modified SVM algorithm for the classification of data augmented with explicit quality quantification for each example in the training set. As the extension to nonlinear decision functions through the use of kernels brings to a non-convex optimization problem, we develop an approximate solution. Finally, the proposed approach is applied to a set of benchmarks and contrasted with analogous methodologies in the literature.
A Modified SVM Classification Algorithm for Data of Variable Quality / B. Apolloni, D. Malchiodi, L. Natali - In: Knowledge-Based Intelligent Information and Engineering Systems : KES 2007 - WIRN 2007 : 11th International Conference, KES 2007, XVII Italian Workshop on Neural Networks Vietri sul Mare, Italy, September 12-14, 2007 : Proceedings. Part III / [a cura di] B. Apolloni, R. Howlett and L. Jain. - Berlin : Springer, 2007. - ISBN 9783540748281. - pp. 131-139 (( convegno joint conferences of KES 2007 tenutosi a Vietri sul Mare (SA), Italy nel 2007.
A Modified SVM Classification Algorithm for Data of Variable Quality
B. ApolloniPrimo
;D. MalchiodiSecondo
;
2007
Abstract
We propose a modified SVM algorithm for the classification of data augmented with explicit quality quantification for each example in the training set. As the extension to nonlinear decision functions through the use of kernels brings to a non-convex optimization problem, we develop an approximate solution. Finally, the proposed approach is applied to a set of benchmarks and contrasted with analogous methodologies in the literature.
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