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| Content Provider | ACM Digital Library |
|---|---|
| Author | Oliveira, Arlindo Silveira, L. Miguel Afonso, João Phillips, Joel |
| Abstract | In this paper we explore the potential of using a general class offunctional representation techniques, kernel-based regression, inthe nonlinear model reduction problem. The kernel-based view-pointprovides a convenient computational framework for regression,unifying and extending the previously proposed polynomialand piecewise-linear reduction methods. Furthermore, as many familiarmethods for linear system manipulation can be leveraged ina nonlinear context, kernels provide insight into how new, morepowerful, nonlinear modeling strategies can be constructed. Wepresent an SVD-like technique for automatic compression of non-linearmodels that allows systematic identification of model redundanciesand rigorous control of approximation error. |
| File Format | |
| ISBN | 1581137621 |
| ISSN | 10923152 |
| DOI | 10.1109/ICCAD.2003.31 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2003-11-09 |
| Access Restriction | Subscribed |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Graphics and Computer-Aided Design Software Computer Science Applications |
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