Abstract
All previous work in inductive inference and theoretical machine learning has taken the perspective of looking for a learning algorithm that successfully learns a collection of functions. In this work, we consider the perspective of starting with a set of functions, and considering the collection of learning algorithms that are successful at learning the given functions. Some strong dualities are revealed.
This work was facilitated by an international agreement under NSF Grant 9119540.
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© 1993 Springer-Verlag Berlin Heidelberg
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Freivalds, R., Smith, C.H. (1993). On the duality between mechanistic learners and what it is they learn. In: Jantke, K.P., Kobayashi, S., Tomita, E., Yokomori, T. (eds) Algorithmic Learning Theory. ALT 1993. Lecture Notes in Computer Science, vol 744. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-57370-4_43
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DOI: https://doi.org/10.1007/3-540-57370-4_43
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