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» PAC Learning from Positive Statistical Queries
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COLT
2001
Springer
13 years 10 months ago
On Using Extended Statistical Queries to Avoid Membership Queries
The Kushilevitz-Mansour (KM) algorithm is an algorithm that finds all the “large” Fourier coefficients of a Boolean function. It is the main tool for learning decision trees ...
Nader H. Bshouty, Vitaly Feldman
CORR
2006
Springer
99views Education» more  CORR 2006»
13 years 5 months ago
PAC Learning Mixtures of Axis-Aligned Gaussians with No Separation Assumption
Abstract. We propose and analyze a new vantage point for the learning of mixtures of Gaussians: namely, the PAC-style model of learning probability distributions introduced by Kear...
Jon Feldman, Ryan O'Donnell, Rocco A. Servedio
FSTTCS
2004
Springer
13 years 10 months ago
Learning Languages from Positive Data and a Finite Number of Queries
A computational model for learning languages in the limit from full positive data and a bounded number of queries to the teacher (oracle) is introduced and explored. Equivalence, ...
Sanjay Jain, Efim B. Kinber
ICGI
2010
Springer
13 years 6 months ago
Polynomial-Time Identification of Multiple Context-Free Languages from Positive Data and Membership Queries
This paper presents an efficient algorithm that identifies a rich subclass of multiple context-free languages in the limit from positive data and membership queries by observing wh...
Ryo Yoshinaka
STOC
2000
ACM
174views Algorithms» more  STOC 2000»
13 years 9 months ago
Noise-tolerant learning, the parity problem, and the statistical query model
We describe a slightly subexponential time algorithm for learning parity functions in the presence of random classification noise, a problem closely related to several cryptograph...
Avrim Blum, Adam Kalai, Hal Wasserman