Learning fixed-dimension linear thresholds from fragmented data
type: Item
instance of: scholarly article
title: Learning fixed-dimension linear thresholds from fragmented data
zbMATH Open document ID: 1005.68085
author: Paul W. Goldberg
published in: Information and Computation
publication date: 2003-01-14T00:00:00Z
full work available at URL: WRAP cs-rr-362.pdf
Mathematics Subject Classification ID: 68Q32
zbMATH DE Number: 1853215
MaRDI profile type: Publication
OpenAlex ID: W2126356189
cites work: Q4004178
cites work: Covering numbers for real-valued function classes
cites work: On learning a union of half spaces
cites work: Learning with restricted focus of attention
cites work: Q4470556
cites work: Learning by distances
cites work: On restricted-focus-of-attention learnability of Boolean functions
cites work: Learning with unreliable boundary queries
cites work: A polynomial-time algorithm for learning noisy linear threshold functions
cites work: Learnability and the Vapnik-Chervonenkis dimension
cites work: Harmonic Analysis of Polynomial Threshold Functions
cites work: Exact Learning of Discretized Geometric Concepts
cites work: Noise-tolerant distribution-free learning of general geometric concepts
cites work: Q4139463
cites work: A Mildly Exponential Time Algorithm for Approximating the Number of Solutions to a Multidimensional Knapsack Problem
cites work: Linear programming — Randomization and abstract frameworks
cites work: Can PAC learning algorithms tolerate random attribute noise?
cites work: Decision theoretic generalizations of the PAC model for neural net and other learning applications
cites work: Probability Inequalities for Sums of Bounded Random Variables
cites work: Efficient noise-tolerant learning from statistical queries
cites work: On the learnability of discrete distributions
cites work: Efficient distribution-free learning of probabilistic concepts
cites work: Q3816821
cites work: Q5341273
cites work: Convergence of stochastic processes
cites work: Comparison of Discrimination Techniques Applied to a Complex Data Set of Head Injured Patients
cites work: A theory of the learnable
DOI: 10.1006/INCO.2001.3059
description: scientific article; zbMATH DE number 1853215