Machine Learning
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Sparse on-line Gaussian processes
Neural Computation
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Predictive Approaches for Choosing Hyperparameters in Gaussian Processes
Neural Computation
Regularization in the selection of radial basis function centers
Neural Computation
Adaptive spherical Gaussian kernel in sparse Bayesian learning framework for nonlinear regression
Expert Systems with Applications: An International Journal
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We propose a fast, incremental algorithm for designing linear regression models. The proposed algorithm generates a sparse model by optimizing multiple smoothing parameters using the generalized cross-validation approach. The performances on synthetic and real-world data sets are compared with other incremental algorithms such as Tipping and Faul's fast relevance vector machine, Chen et al.'s orthogonal least squares, and Orr's regularized forward selection. The results demonstrate that the proposed algorithm is competitive.