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"Optimization never "learns"....it just follows the instructions provided
and a final combination of input parameters is
determined optimal."
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Why do you believe optimization never learns? In my opinion, not only is
learning a form of optimization, but that learning itself can be designed
to become more efficient over time, which is a form of meta-optimization.
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Bottom line: for complex data, neural nets are best and definitely more
efficient and elegant.
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Some I/O mapping techniques are more efficient than others, depending on
the relationship between the independent and dependent variables. NNs are
not categorically the best, most efficient nor most elegant. However, when
given proper input variables, NNs usually succeed in learning (optimizing)
the I/O map. It's the creation of those "proper" inputs that take the most
effort.
Regards,
Mark Jurik
Jurik Research
http://www.jurikres.com
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