Radial Basis Function Network for Multi-task Learning

Part of Advances in Neural Information Processing Systems 18 (NIPS 2005)

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Xuejun Liao, Lawrence Carin


We extend radial basis function (RBF) networks to the scenario in which multiple correlated tasks are learned simultaneously, and present the cor- responding learning algorithms. We develop the algorithms for learn- ing the network structure, in either a supervised or unsupervised manner. Training data may also be actively selected to improve the network’s gen- eralization to test data. Experimental results based on real data demon- strate the advantage of the proposed algorithms and support our conclu- sions.