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Title: Model assessment with renormalization group in statistical learning
Authors: Wang, Q.-G. 
Yu, C.
Zhang, Y.
Issue Date: 2013
Citation: Wang, Q.-G.,Yu, C.,Zhang, Y. (2013). Model assessment with renormalization group in statistical learning. IEEE International Conference on Control and Automation, ICCA : 884-889. ScholarBank@NUS Repository.
Abstract: This paper proposes a new method for model assessment based on Renormalization Group. Renormalization Group is applied to the original data set to obtain the transformed data set with the majority rule to set its labels. The assessment is first performed on the data level without invoking any learning method, and the consistency and non-randomness indices are defined by comparing two data sets to reveal informative content of the data. When the indices indicate informative data, the next assessment is carried out at the model level, and the predictions are compared between two models learnt from the original and transformed data sets, respectively. The model consistency and reliability indices are introduced accordingly. Unlike cross-validation and other standard methods in the literature, the proposed method creates a new data set and data assessment. Besides, it requires only two models and thus less computational burden for model assessment. The proposed method is illustrated with academic and practical examples. © 2013 IEEE.
Source Title: IEEE International Conference on Control and Automation, ICCA
ISBN: 9781467347075
ISSN: 19483449
DOI: 10.1109/ICCA.2013.6565152
Appears in Collections:Staff Publications

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