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» PAC Learning from Positive Statistical Queries
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COLT
2001
Springer
15 years 4 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»
14 years 11 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
15 years 5 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
15 years 22 days 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»
15 years 4 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