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» Hardness of Minimizing and Learning DNF Expressions
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STOC
2006
ACM
170views Algorithms» more  STOC 2006»
14 years 5 months ago
Hardness of approximate two-level logic minimization and PAC learning with membership queries
Producing a small DNF expression consistent with given data is a classical problem in computer science that occurs in a number of forms and has numerous applications. We consider ...
Vitaly Feldman
FOCS
2008
IEEE
13 years 11 months ago
Hardness of Minimizing and Learning DNF Expressions
We study the problem of finding the minimum size DNF formula for a function f : {0, 1}d → {0, 1} given its truth table. We show that unless NP ⊆ DTIME(npoly(log n) ), there i...
Subhash Khot, Rishi Saket
FOCS
2006
IEEE
13 years 11 months ago
New Results for Learning Noisy Parities and Halfspaces
We address well-studied problems concerning the learnability of parities and halfspaces in the presence of classification noise. Learning of parities under the uniform distributi...
Vitaly Feldman, Parikshit Gopalan, Subhash Khot, A...
NIPS
2001
13 years 6 months ago
Efficiency versus Convergence of Boolean Kernels for On-Line Learning Algorithms
The paper studies machine learning problems where each example is described using a set of Boolean features and where hypotheses are represented by linear threshold elements. One ...
Roni Khardon, Dan Roth, Rocco A. Servedio
COLT
2003
Springer
13 years 10 months ago
Maximum Margin Algorithms with Boolean Kernels
Recent work has introduced Boolean kernels with which one can learn linear threshold functions over a feature space containing all conjunctions of length up to k (for any 1 ≤ k ...
Roni Khardon, Rocco A. Servedio