Too Much Information Can be Too Much for Learning Efficiently

Authors: Rolf Wiehagen and Thomas Zeugmann

Source: “Analogical and Inductive Inference, AII '92, Dagstuhl Castle, Germany, October 1992, Proceedings,” (K.P. Jantke, ed.), Lecture Notes in Artificial Intelligence 642, pp. 72 - 86, Springer-Verlag 1992.

Abstract. In designing learning algorithms it seems quite reasonable to construct them in a way such that all data the algorithm already has obtained are correctly and completely reflected in the description the algorithm outputs on these data. However, this approach may totally fail, i.e., it may lead to the unsolvability of the learning problem, or it may exclude any efficient solution of it. In particular, we present a natural learning problem and prove that it can be solved in polynomial time if and only if the algorithm is allowed to ignore data.

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