Local experts combination through density decomposition.
Ahmed Rida, Abderrahim Labbi, Christian Pellegrini · 1999
In this paper we describe a divide-andcombine strategy for decomposition of a complex prediction problem into simpler local sub-problems. We firstly show how to perform a soft decomposition via clustering of input data. Such decomposition leads to a partition of the input space into several regions which may overlap. Therefore, to each region is assigned a local predictor (or expert) which is trained only on local data. To construct a solution to the global prediction problem, we combine the local experts using two approaches: weighted averaging where the outputs of local experts are weighted by their prior densities, and nonlinear adaptive combination where the pooling parameters are obtained through minimization of a global error. To illustrate the validity of our approach, we show simulation results for two classification tasks, vowels and phonemes, using local experts which are Multi-Layer Perceptrons (MLP) and Support Vector Machines (SVM). We compare the results obtained using...