Multi-category classifiers and sample width

DOI10.1016/j.jcss.2016.04.003zbMath1348.68179OpenAlexW2342680140MaRDI QIDQ736602

Martin Anthony, Joel Ratsaby

Publication date: 4 August 2016

Published in: Journal of Computer and System Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jcss.2016.04.003

zbMATH Keywords

pattern recognitionmachine learninggeneralization errormulti-category classification

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10)

Related Items (3)

Large width nearest prototype classification on general distance spaces ⋮ Learning half-spaces on general infinite spaces equipped with a distance function ⋮ Large-width bounds for learning half-spaces on distance spaces

Cites Work

• Analysis of a multi-category classifier
• Maximal width learning of binary functions
• Decision theoretic generalizations of the PAC model for neural net and other learning applications
• A probabilistic approach to case-based inference
• Learning bounds via sample width for classifiers on finite metric spaces
• Learnability and the Vapnik-Chervonenkis dimension
• Uniform Central Limit Theorems
• Scale-sensitive dimensions, uniform convergence, and learnability
• The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network
• Function Learning from Interpolation
• Structural risk minimization over data-dependent hierarchies
• Neural Network Learning
• On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities
• Convergence of stochastic processes
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: Multi-category classifiers and sample width