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Abstract

We give an algorithm that with high probability properly learns random monotone DNF with t(n) terms of length ≈ logt(n) under the uniform distribution on the Boolean cube {0,1}n. For any function t(n) ≤ poly(n) the algorithm runs in time poly(n,1/ε) and with high probability outputs an ε-accurate monotone DNF hypothesis. This is the first algorithm that can learn monotone DNF of arbitrary polynomial size in a reasonable average-case model of learning from random examples only.

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Ashish Goel Klaus Jansen José D. P. Rolim Ronitt Rubinfeld

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© 2008 Springer-Verlag Berlin Heidelberg

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Jackson, J.C., Lee, H.K., Servedio, R.A., Wan, A. (2008). Learning Random Monotone DNF. In: Goel, A., Jansen, K., Rolim, J.D.P., Rubinfeld, R. (eds) Approximation, Randomization and Combinatorial Optimization. Algorithms and Techniques. APPROX RANDOM 2008 2008. Lecture Notes in Computer Science, vol 5171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85363-3_38

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  • DOI: https://doi.org/10.1007/978-3-540-85363-3_38

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-85362-6

  • Online ISBN: 978-3-540-85363-3

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