Radial Basis Function Network for Multi-task Learning

We extend radial basis function (RBF) networks to the scenario in which multiple correlated tasks are learned simultaneously, and present the corresponding learningalgorithms. We develop the algorithms for learning the network structure, in either a supervised or unsupervised manner. Training data may also be actively selected to improve the network's generalization totest data. Experimental results based on real data demonstrate the advantage of the proposed algorithms and support our conclusions.