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Using Support Vector Machines to Predict the Performance of MLP Neural Networks

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3 Author(s)
Ricardo B. C. PrudĂȘncio ; Center of Inf., Fed. Univ. of Pernambuco, Recife ; Silvio B. Guerra ; Teresa B. Ludermir

In this work, we investigated the use of support vector machines (SVM) to predict the performance of learning algorithms based on features of the learning problems, in a kind of meta-learning. Experiments were performed in a case study in which SVM regressors with different kernel functions were used to predict the performance of multi-layer perceptron (MLP) networks. The results obtained on a set of 50 learning problems revealed that the SVMs obtained better results in predicting the MLP performance,when compared to benchmark algorithms applied in previous work.

Published in:

2008 10th Brazilian Symposium on Neural Networks

Date of Conference:

26-30 Oct. 2008