Learning fixed-dimension linear thresholds from fragmented data
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Publication:1854473
DOI10.1006/inco.2001.3059zbMath1005.68085OpenAlexW2126356189MaRDI QIDQ1854473
Publication date: 14 January 2003
Published in: Information and Computation (Search for Journal in Brave)
Full work available at URL: http://wrap.warwick.ac.uk/61089/7/WRAP_cs-rr-362.pdf
Cites Work
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- On learning a union of half spaces
- Decision theoretic generalizations of the PAC model for neural net and other learning applications
- A polynomial-time algorithm for learning noisy linear threshold functions
- Learning with unreliable boundary queries
- Learning with restricted focus of attention
- Efficient distribution-free learning of probabilistic concepts
- On restricted-focus-of-attention learnability of Boolean functions
- Learning by distances
- Can PAC learning algorithms tolerate random attribute noise?
- On the learnability of discrete distributions
- Noise-tolerant distribution-free learning of general geometric concepts
- Harmonic Analysis of Polynomial Threshold Functions
- Learnability and the Vapnik-Chervonenkis dimension
- A theory of the learnable
- Comparison of Discrimination Techniques Applied to a Complex Data Set of Head Injured Patients
- Exact Learning of Discretized Geometric Concepts
- A Mildly Exponential Time Algorithm for Approximating the Number of Solutions to a Multidimensional Knapsack Problem
- Covering numbers for real-valued function classes
- Linear programming — Randomization and abstract frameworks
- Efficient noise-tolerant learning from statistical queries
- Probability Inequalities for Sums of Bounded Random Variables
- Convergence of stochastic processes
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