Conceptual inductive learning. The case of unreliable teachers

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Various algorithms for learning from examples usually suppose more or less reliable sources of information. In this paper, we study the influence of unreliable information sources on the learning process and the recovery possibilities. The problem is analyzed within the frame of the rough set theory which seems to be a suitable means for treating incomplete and uncertain knowledge. We briefly report on the learning system FLORA which is based on this theory.

Original languageEnglish
Pages (from-to)169-182
Number of pages14
JournalArtificial Intelligence
Volume52
Issue number2
DOIs
StatePublished - Jan 1 1991
Externally publishedYes

Fingerprint

Rough set theory
source of information
Learning systems
Recovery
set theory
teacher
learning
learning process
Incomplete
Learning Process
Learning Systems
Set Theory

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics

Cite this

Conceptual inductive learning. The case of unreliable teachers. / Kubat, Miroslav.

In: Artificial Intelligence, Vol. 52, No. 2, 01.01.1991, p. 169-182.

Research output: Contribution to journalArticle

@article{9c82637d371345118c7b1a8c4f9ee41c,
title = "Conceptual inductive learning. The case of unreliable teachers",
abstract = "Various algorithms for learning from examples usually suppose more or less reliable sources of information. In this paper, we study the influence of unreliable information sources on the learning process and the recovery possibilities. The problem is analyzed within the frame of the rough set theory which seems to be a suitable means for treating incomplete and uncertain knowledge. We briefly report on the learning system FLORA which is based on this theory.",
author = "Miroslav Kubat",
year = "1991",
month = "1",
day = "1",
doi = "10.1016/0004-3702(91)90041-H",
language = "English",
volume = "52",
pages = "169--182",
journal = "Artificial Intelligence",
issn = "0004-3702",
publisher = "Elsevier",
number = "2",

}

TY - JOUR

T1 - Conceptual inductive learning. The case of unreliable teachers

AU - Kubat, Miroslav

PY - 1991/1/1

Y1 - 1991/1/1

N2 - Various algorithms for learning from examples usually suppose more or less reliable sources of information. In this paper, we study the influence of unreliable information sources on the learning process and the recovery possibilities. The problem is analyzed within the frame of the rough set theory which seems to be a suitable means for treating incomplete and uncertain knowledge. We briefly report on the learning system FLORA which is based on this theory.

AB - Various algorithms for learning from examples usually suppose more or less reliable sources of information. In this paper, we study the influence of unreliable information sources on the learning process and the recovery possibilities. The problem is analyzed within the frame of the rough set theory which seems to be a suitable means for treating incomplete and uncertain knowledge. We briefly report on the learning system FLORA which is based on this theory.

UR - http://www.scopus.com/inward/record.url?scp=30244534052&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=30244534052&partnerID=8YFLogxK

U2 - 10.1016/0004-3702(91)90041-H

DO - 10.1016/0004-3702(91)90041-H

M3 - Article

VL - 52

SP - 169

EP - 182

JO - Artificial Intelligence

JF - Artificial Intelligence

SN - 0004-3702

IS - 2

ER -