Prudencio, R.B.C.,  Guerra, S.B.,  Ludermir, T.B.. 

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.

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4665916

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