Data Mining via Redes Neuronais Artificiais e Máquinas de Vectores de Suporte

this paper pretends to infer about the advantages of two nonlinear data mining models: artificial neural networks (ann) and support vector machines (svm). in particular, the intention is to measure their performance when applied to classification and regression tasks, being compared with other techniques (i.e. decision/regression trees). thus, an analysis was performed over a wide range of software tools that implement the referred models. from this set, two open-source applications (i.e. r environment and weka) where selected to conduct the experiments. several real world problems where used as benchmarks. the results show that in general the svm achieves better forecasts, followed by the ann.