Optimal Rates for Regularization Operators in Learning Theory
We develop some new error bounds for learning algorithms induced by regularization methods in the regression setting. The "hardness" of the problem is characterized in terms of the parameters r and s, the first related to the "complexity" of the target function, the second conne...
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| Language: | en_US |
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2006
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| Online Access: | http://hdl.handle.net/1721.1/34216 |