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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Qian Zhang Ki Jung Lee Taeg Keun Whangbo |
| Copyright Year | 2007 |
| Description | Author affiliation: Kyungwon Univ., Songnam (Qian Zhang; Ki Jung Lee; Taeg Keun Whangbo) |
| Abstract | The Sasang typology is the traditional typology theory in Oriental Medicine. The medical typology distributes people into four types based on their traits: Tae-Yang, So-Yang, Tae-Eum and So-Eum type. In the paper, we design a model for Sasang typology classification based on the least squares support vector machines (LS-SVM). We use k-mean algorithm for decision the feature index from the side face, then we add the side face ratios into our feature space for improvement. The choice of resample method is very important for parameters' optimization in SVM and it will influence the system's stability. We propose a novel resampling algorithm called double cross-validation through comparing the cross-validation with bootstrap approach. The result shows that the established model under double cross- validation algorithm based on LS-SVM is more robust with high performance and suffices for the requirements of control and optimization for classification processes. |
| Starting Page | 426 |
| Ending Page | 430 |
| File Size | 193101 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781424415311 |
| DOI | 10.1109/ICAL.2007.4338600 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-08-18 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Design methodology Optimization methods Artificial neural networks Double Cross-Validation Classification algorithms Least squares methods Support vector machines Support vector machine classification Clustering algorithms Pattern classification K-mean algorithm Bootstrap Least Squares Support Vector Machines Mathematical model Sasang Typology Classification |
| Content Type | Text |
| Resource Type | Article |
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