This article describes the competitive associative net called CAN2 and cross-validation which we have used for making prediction and estimating predictive uncertainty on the regression problems at the Evaluating Predictive Uncertainty Challenge. The CAN2 with an efficient batch learning method for reducing empirical (training) error is combined with cross-validation for making prediction (generalization) error small and estimating predictive distribution accurately. From an analogy of Bayesian learning, a stochastic analysis is derived to indicate a validity of our method. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Kurogi, S., Sawa, M., & Tanaka, S. (2006). Competitive associative nets and cross-validation for estimating predictive uncertainty on regression problems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3944 LNAI, pp. 78–94). https://doi.org/10.1007/11736790_6
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