A resource-allocating network for function interpolation
Neural Computation
2005 Special issue: Incremental learning of feature space and classifier for face recognition
Neural Networks - 2005 Special issue: IJCNN 2005
On-line EM Algorithm for the Normalized Gaussian Network
Neural Computation
Incremental Leaning and Model Selection for Radial Basis Function Network through Sleep
IEICE - Transactions on Information and Systems
Fast learning in networks of locally-tuned processing units
Neural Computation
Multivariate Student-t self-organizing maps
Neural Networks
Incremental learning methods with retrieving of interfered patterns
IEEE Transactions on Neural Networks
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Model selection for machine learning systems is one of the most important issues to be addressed for obtaining greater generalization capabilities. This paper proposes a strategy to achieve model selection incrementally under virtual concept drifting environments, where the distribution of learning samples varies over time. To carry out incremental model selection, the system generally uses all the learning samples that have been observed until now. Under virtual concept drifting environments, however, the distribution of the observed samples is considerably different from that under real concept drifting environments so that model selection is usually unsuccessful. To overcome this problem, the author had earlier proposed the weighted objective function and model-selection criterion based on the predictive input density of the learning samples. Although the previous method described in the author's previous study shows good performances to some datasets, it occasionally fails to yield appropriate learning results because of the failure in the prediction of the actual input density. To overcome this drawback, the method proposed in this paper improves on the previously described method to yield the desired outputs using an ensemble of the constructed radial basis function neural networks (RBFNNs). Experimental results indicate that the improved method yields a stable performance.