nips nips2009 nips2009-154 nips2009-154-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Hongjing Lu, Matthew Weiden, Alan L. Yuille
Abstract: We develop a Bayesian sequential model for category learning. The sequential model updates two category parameters, the mean and the variance, over time. We define conjugate temporal priors to enable closed form solutions to be obtained. This model can be easily extended to supervised and unsupervised learning involving multiple categories. To model the spacing effect, we introduce a generic prior in the temporal updating stage to capture a learning preference, namely, less change for repetition and more change for variation. Finally, we show how this approach can be generalized to efficiently perform model selection to decide whether observations are from one or multiple categories.
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