Abstract
We show that random DNF formulas, random log-depth decision trees and random deterministic finite acceptors cannot be weakly learned with a polynomial number of statistical queries with respect to an arbitrary distribution on examples.
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Angluin, D., Eisenstat, D., Kontorovich, L.(., Reyzin, L. (2010). Lower Bounds on Learning Random Structures with Statistical Queries. In: Hutter, M., Stephan, F., Vovk, V., Zeugmann, T. (eds) Algorithmic Learning Theory. ALT 2010. Lecture Notes in Computer Science(), vol 6331. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16108-7_18
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DOI: https://doi.org/10.1007/978-3-642-16108-7_18
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