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