Measure Fields for Function Approximation
The computation of a piecewise smooth function that approximates a finite set of data points may be decomposed into two decoupled tasks: first, the computation of the locally smooth models, and hence, the segmentation of the data into classes that consist on the sets of points best approximat...
| Main Author: | |
|---|---|
| Language: | en_US |
| Published: |
2004
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/1721.1/7211 |
| _version_ | 1811086938921238528 |
|---|---|
| author | Marroquin, Jose L. |
| author_facet | Marroquin, Jose L. |
| author_sort | Marroquin, Jose L. |
| collection | MIT |
| description | The computation of a piecewise smooth function that approximates a finite set of data points may be decomposed into two decoupled tasks: first, the computation of the locally smooth models, and hence, the segmentation of the data into classes that consist on the sets of points best approximated by each model, and second, the computation of the normalized discriminant functions for each induced class. The approximating function may then be computed as the optimal estimator with respect to this measure field. We give an efficient procedure for effecting both computations, and for the determination of the optimal number of components. |
| first_indexed | 2024-09-23T13:37:12Z |
| id | mit-1721.1/7211 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T13:37:12Z |
| publishDate | 2004 |
| record_format | dspace |
| spelling | mit-1721.1/72112019-04-10T11:52:46Z Measure Fields for Function Approximation Marroquin, Jose L. function approximation classification neural networks The computation of a piecewise smooth function that approximates a finite set of data points may be decomposed into two decoupled tasks: first, the computation of the locally smooth models, and hence, the segmentation of the data into classes that consist on the sets of points best approximated by each model, and second, the computation of the normalized discriminant functions for each induced class. The approximating function may then be computed as the optimal estimator with respect to this measure field. We give an efficient procedure for effecting both computations, and for the determination of the optimal number of components. 2004-10-20T20:49:55Z 2004-10-20T20:49:55Z 1993-06-01 AIM-1433 CBCL-091 http://hdl.handle.net/1721.1/7211 en_US AIM-1433 CBCL-091 21 p. 2521920 bytes 1964059 bytes application/postscript application/pdf application/postscript application/pdf |
| spellingShingle | function approximation classification neural networks Marroquin, Jose L. Measure Fields for Function Approximation |
| title | Measure Fields for Function Approximation |
| title_full | Measure Fields for Function Approximation |
| title_fullStr | Measure Fields for Function Approximation |
| title_full_unstemmed | Measure Fields for Function Approximation |
| title_short | Measure Fields for Function Approximation |
| title_sort | measure fields for function approximation |
| topic | function approximation classification neural networks |
| url | http://hdl.handle.net/1721.1/7211 |
| work_keys_str_mv | AT marroquinjosel measurefieldsforfunctionapproximation |