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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Qing-Guo Wang Chao Yu Yong Zhang |
| Copyright Year | 2013 |
| Description | Author affiliation: Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore, Singapore (Qing-Guo Wang; Chao Yu) || Qiming Venture Partners, Shanghai, China (Yong Zhang) |
| 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 nonrandomness 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. |
| Starting Page | 884 |
| Ending Page | 889 |
| File Size | 1806123 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781467347075 |
| ISSN | 19483449 |
| e-ISBN | 9781467347082 |
| DOI | 10.1109/ICCA.2013.6565152 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-06-12 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Data models Indexes Computational modeling Predictive models Reliability Learning systems Hypercubes |
| Content Type | Text |
| Resource Type | Article |
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