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Old 01-09-2013, 04:44 PM
cygnids cygnids is offline
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Default Learning Approach vs. Function Approximation

I'm trying to firm my sense of differences in the way functions are generally sought using the learning paradigm vs. the classical function approximation approaches. Ie, is it the same, or are there differences in thought/approach.

As I see it, seeking linear models in supervised learning seems no different from similar methods in function approximation (variations in error measures and their minimization aside). In both instances we use input-output sets to accomplish the task, ie find an approximant. In learning, we call the underlying generative process the target function, and after learning, the resulting approximant, the hypothesis. Approximation theory has it's own lingo.

I suspect there must be more to this than my superficial characterization. So, in general, do approaches adopted in supervised learning run in parallel to approaches in function approximation? Are there some philosophical differences?

If someone would kindly suggest how & why I should view them as separate developments, I'd much appreciate it. Thank you.
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