Predicting the Performance of Learning Algorithms Using Support Vector Machines as Meta-Regressors

Abstract. In this work, we proposed the use of Support Vector Ma-chines (SVM) to predict the performance of machine learning algorithms based on features of the learning problems. This work is related to the Meta-Regression approach, which has been successfully applied to pre-dict learning performance, supporting algorithm selection. Experiments were performed in a case study in which SVMs with different kernel functions were used to predict the performance of Multi-Layer Percep-tron (MLP) networks. The SVMs obtained better results in the evaluated task, when compared to different algorithms that have been applied as meta-regressors in previous work.