Competitive associative nets and cross-validation for estimating predictive uncertainty on regression problems

Authors:
Shuichi Kurogi;Miho Sawa;Shinya Tanaka
Affiliations:
Kyushu Institute of Technology, Kitakyushu Fukuoka, Japan;Kyushu Institute of Technology, Kitakyushu Fukuoka, Japan;Kyushu Institute of Technology, Kitakyushu Fukuoka, Japan
Venue:
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
Year:
2005

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Abstract

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.