Learning Distributions by Their Density Levels: A Paradigm for Learning without a Teacher
- 31 August 1997
- journal article
- Published by Elsevier in Journal of Computer and System Sciences
- Vol. 55 (1), 171-182
- https://doi.org/10.1006/jcss.1997.1507
Abstract
No abstract availableKeywords
This publication has 15 references indexed in Scilit:
- Lower bounds for sampling algorithms for estimating the averageInformation Processing Letters, 1995
- Polynomial bounds for VC dimension of sigmoidal neural networksPublished by Association for Computing Machinery (ACM) ,1995
- Bounding the Vapnik-Chervonenkis dimension of concept classes parameterized by real numbersMachine Learning, 1995
- On the learnability of discrete distributionsPublished by Association for Computing Machinery (ACM) ,1994
- Localization vs. identification of semi-algebraic setsPublished by Association for Computing Machinery (ACM) ,1993
- Learnability with respect to fixed distributionsTheoretical Computer Science, 1991
- Learnability and the Vapnik-Chervonenkis dimensionJournal of the ACM, 1989
- A general lower bound on the number of examples needed for learningInformation and Computation, 1989
- A survey of the hough transformComputer Vision, Graphics, and Image Processing, 1988
- Learning from noisy examplesMachine Learning, 1988