| REPORT DETAIL |
| Id. report: |
LSI-02-11-R |
| Title: |
On-line Support Vector Machines for Function Approximation
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| Author(s): |
Martín, M. |
| Date (year-month-day): |
2002-02-05 |
| Scope: |
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| Document Type: |
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| Keyword(s): |
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| Abstract: |
This paper describes an on-line method for building
epsilon-insensitive support vector machines for regression as
described in (Vapnik, 1995). The method is an extension of the method
developed by (Cauwenberghs & Poggio, 2000) for building incremental
support vector machines for classification. Machines obtained by
using this approach are equivalent to the ones obtained by applying
exact methods like quadratic programming, but they are obtained more
quickly and allow the incremental addition of new points, removal of
existing points and update of target values for existing data. This
development opens the application of SVM regression to areas such as
on-line prediction of temporal series or generalization of value
functions in reinforcement learning.
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| Comments: |
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| Languaje: |
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| Document properties |
| Number of pages: |
11 |
| Size: |
155.85 KB |
| File type: |
pdf (Zipped) |
| Download: |
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