Mining Dependence Structures from Statistical Learning Perspective

Authors:
Lei Xu
Affiliations:
-
Venue:
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
Year:
2002

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Abstract

Mining various dependence structures from data are important to many data mining applications. In this paper, several major dependence structure mining tasks are overviewed from statistical learning perspective, with a number of major results on unsupervised learning models that range from a single-object world to a multi-object world. Moreover, efforts towards a key challenge to learning have been discussed in three typical streams, based on generalization error bounds, Ockham principle, and BYY harmony learning, respectively.