Some connections between learning and optimization
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Publication:1885804
DOI10.1016/j.dam.2004.06.005zbMath1075.68630OpenAlexW1977837160MaRDI QIDQ1885804
Publication date: 12 November 2004
Published in: Discrete Applied Mathematics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.dam.2004.06.005
Learning and adaptive systems in artificial intelligence (68T05) Combinatorial optimization (90C27) Randomized algorithms (68W20)
Cites Work
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- Estimation of dependences based on empirical data. Transl. from the Russian by Samuel Kotz
- Equivalence of models for polynomial learnability
- Decision theoretic generalizations of the PAC model for neural net and other learning applications
- The densest hemisphere problem
- Sharper bounds for Gaussian and empirical processes
- Toward efficient agnostic learning
- Hardness results for neural network approximation problems
- A general lower bound on the number of examples needed for learning
- Robust trainability of single neurons
- General bounds on the number of examples needed for learning probabilistic concepts
- Learnability and the Vapnik-Chervonenkis dimension
- A theory of the learnable
- Computational limitations on learning from examples
- Cryptographic limitations on learning Boolean formulae and finite automata
- Neural Network Learning
- On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities
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