Author: Dan Andersson
Date: 13:01:27 03/10/04
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Automated learning is a fun field of research. The problem must almost always be operationalized/categorized/limited in some way. Global optimization is always possible, if not always feasible, inside a given framework. The following is an argument of practicality and effifacy: I.e. You can always train evaluation weights inside a fixed search algorithm. It might possibly do progress if you train eval weights as well as extensions at the same time. But it doesn't follow that such training will progress faster that training extensions and eval terms separately. And then combining those two optimizations. The same might be true for categories of evaluation terms depending on the choice of those terms. MvH Dan Andersson
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