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Abstract-Supervised learning techniques can be roughly grouped into lazy learning or eager learning. Lazy learning and eager learning have very different properties and are suitable for different applications. In this paper we evaluate properties of the two types of learning using a representative distance based algorithm for each class, namely, kNN (k-nearest neighbors) and RBFN (Radial Basis Function Network). In addition, an edition algorithm (SPAM -Supervised Partitioning Around Medoids) is used to reduce the labeled dataset. Our experiments for classification and regression tasks, using 12 public datasets show that prototype selection algorithms typically used with kNN are good alternatives for selection of centers of RBFN when to optimize the number of centers is not the relevant criterion. The experiments also show that the RBFN generally perform better than Edited kNN.

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