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ICML
2005
IEEE
14 years 11 months ago
Why skewing works: learning difficult Boolean functions with greedy tree learners
We analyze skewing, an approach that has been empirically observed to enable greedy decision tree learners to learn "difficult" Boolean functions, such as parity, in the...
Bernard Rosell, Lisa Hellerstein, Soumya Ray, Davi...
ACL
2006
13 years 11 months ago
From Prosodic Trees to Syntactic Trees
This paper describes an ongoing effort to parse the Hebrew Bible. The parser consults the bracketing information extracted from the cantillation marks of the Masoetic text. We fir...
Andi Wu, Kirk Lowery
CORR
2011
Springer
155views Education» more  CORR 2011»
13 years 5 months ago
Subexponential convergence for information aggregation on regular trees
— We consider the decentralized binary hypothesis testing problem on trees of bounded degree and increasing depth. For a regular tree of depth t and branching factor k ≥ 2, we ...
Yashodhan Kanoria, Andrea Montanari
ALT
2010
Springer
13 years 11 months ago
Lower Bounds on Learning Random Structures with Statistical Queries
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...
Dana Angluin, David Eisenstat, Leonid Kontorovich,...
TOC
2010
123views Management» more  TOC 2010»
13 years 4 months ago
Rounds vs. Queries Tradeoff in Noisy Computation
: We show that a noisy parallel decision tree making O(n) queries needs (log n) rounds to compute OR of n bits. This answers a question of Newman [IEEE Conference on Computational ...
Navin Goyal, Michael Saks