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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Rhee Man Kil Imhoi Koo |
| Copyright Year | 2003 |
| Description | Author affiliation: Div. of Appl. Math., Korea Adv. Inst. of Sci. & Technol., Taejeon, South Korea (Rhee Man Kil; Imhoi Koo) |
| Abstract | This paper presents a new form of true risk bounds for the regression of real-valued functions. The goal of machine learning is minimizing the true risk (or general error) for the whole distribution of sample space, not just a set of training samples. However, the true risk cannot be estimated accurately with the finite number of samples. In this sense, we derive the form of true risk bounds which may provide the useful guideline for the optimization of learning models. Through the simulation for the function approximation, we have shown that the prediction of true risk bounds based on the suggested functional norm is well fitted to the empirical data. |
| Sponsorship | Int. Neural Network Soc. IEEE Neural Networks Soc |
| Starting Page | 507 |
| Ending Page | 512 |
| File Size | 403398 |
| Page Count | 6 |
| File Format | |
| ISBN | 0780378989 |
| ISSN | 10987576 |
| DOI | 10.1109/IJCNN.2003.1223398 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2003-07-20 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Kernel Predictive models Function approximation Mathematics Paper technology Machine learning Guidelines Recruitment Convergence Virtual colonoscopy |
| Content Type | Text |
| Resource Type | Article |
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