This paper presents three different learning iterative strategies, in a multi-expert system. In first strategy entire new dataset is used. In second strategy each single classifier selects new samples starting from those on which it performs a misclassification. Finally, the collective behavior of classifiers is studied to select the most profitable samples for knowledge base updating. The experimental results provide a comparison of three approaches under different operating conditions and feedback process. A classifier SVM and four different combination techniques were used by considering the CEDAR (handwritten digit) database. It is shown how results depend by the iterations on the feedback process, as well as by the specific combination decision schema and by data distribution. © 2013 Springer-Verlag.
CITATION STYLE
Barbuzzi, D., Impedovo, D., Mangini, F. M., & Pirlo, G. (2013). Learning iterative strategies in multi-expert systems using SVMs for digit recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8156 LNCS, pp. 121–130). https://doi.org/10.1007/978-3-642-41181-6_13
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