\(P\)-sufficient statistics for PAC learning \(k\)-term-DNF formulas through enumeration
From MaRDI portal
Publication:1978498
DOI10.1016/S0304-3975(98)00215-1zbMath0939.68056MaRDI QIDQ1978498
Claudio Gentile, Bruno Apolloni
Publication date: 4 June 2000
Published in: Theoretical Computer Science (Search for Journal in Brave)
computational complexitysufficient statisticsBoolean functionconcept learningnonparametric statisticslearning by examples
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Fast learning of \(k\)-term DNF formulas with queries.
- Estimation of dependences based on empirical data. Transl. from the Russian by Samuel Kotz
- PAC learning of concept classes through the boundaries of their items
- A general lower bound on the number of examples needed for learning
- On the density of families of sets
- Constant depth circuits, Fourier transform, and learnability
- Learnability and the Vapnik-Chervonenkis dimension
- A theory of the learnable
- Computational limitations on learning from examples
- Learning Monotone Boolean Functions by Uniformly Distributed Examples
- Efficient noise-tolerant learning from statistical queries
- On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities
- A formal theory of inductive inference. Part I
- Some Principles of the Theory of Testing Hypotheses
- A Measure of Asymptotic Efficiency for Tests of a Hypothesis Based on the sum of Observations
This page was built for publication: \(P\)-sufficient statistics for PAC learning \(k\)-term-DNF formulas through enumeration