Learning decision trees from random examples
type: Item
instance of: scholarly article
title: Learning decision trees from random examples
zbMATH Open document ID: 0679.68157
DOI: 10.1016/0890-5401(89)90001-1
author: Andrzej Ehrenfeucht
author: David Haussler
published in: Information and Computation
publication date: 1989-01-01T00:00:00Z
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 90B50
Mathematics Subject Classification ID: 05C05
Mathematics Subject Classification ID: 06E30
zbMATH DE Number: 4114080
zbMATH Keywords: learning
zbMATH Keywords: decision tree
zbMATH Keywords: random examples
zbMATH Keywords: Rivest's polynomial learnability result
MaRDI profile type: Publication
full work available at URL: 0890-5401(89)90001-1
OpenAlex ID: W2029058452
cites work: Q3327527
cites work: Occam's razor
cites work: Learnability and the Vapnik-Chervonenkis dimension
cites work: Quantifying inductive bias: AI learning algorithms and Valiant's learning framework
cites work: Computational limitations on learning from examples
cites work: Inferring decision trees using the minimum description length principle
cites work: A theory of the learnable
description: scientific article; zbMATH DE number 4114080