LEARNING CLASSIFICATION RULES USING BAYES

Wray Buntine

doi:10.1016/B978-1-55860-036-2.50033-3

LEARNING CLASSIFICATION RULES USING BAYES

Wray Buntine

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

27 Citations (Scopus)

Abstract

This paper considers the problem of learning classification rules from data in the context of knowledge acquisition. Bayesian theory provides a framework for both designing learning algorithms and for approaching specific learning applications, for instance, in the selection and tuning of learning tools. Experiments are reported demonstrating how this can be done using two well known learning approaches: simple Bayes classifiers and decision trees.

Original language	English
Title of host publication	Proceedings of the 6th International Workshop on Machine Learning, ICML 1989
Editors	Alberto Maria Segre
Publisher	Morgan Kaufmann Publishers
Pages	94-98
Number of pages	5
ISBN (Electronic)	1558600361, 9781558600362
DOIs	https://doi.org/10.1016/B978-1-55860-036-2.50033-3
Publication status	Published - 1989
Externally published	Yes
Event	International Workshop on Machine Learning 1989 - Ithaca, United States of America Duration: 26 Jun 1989 → 27 Jun 1989 Conference number: 6th https://www.sciencedirect.com/book/edited-volume/9781558600362/proceedings-of-the-sixth-international-workshop-on-machine-learning (Proceedings)

Conference

Conference	International Workshop on Machine Learning 1989
Abbreviated title	ICML 1989
Country/Territory	United States of America
City	Ithaca
Period	26/06/89 → 27/06/89
Internet address	https://www.sciencedirect.com/book/edited-volume/9781558600362/proceedings-of-the-sixth-international-workshop-on-machine-learning (Proceedings)

Access to Document

10.1016/B978-1-55860-036-2.50033-3

Cite this

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title = "LEARNING CLASSIFICATION RULES USING BAYES",

abstract = "This paper considers the problem of learning classification rules from data in the context of knowledge acquisition. Bayesian theory provides a framework for both designing learning algorithms and for approaching specific learning applications, for instance, in the selection and tuning of learning tools. Experiments are reported demonstrating how this can be done using two well known learning approaches: simple Bayes classifiers and decision trees.",

author = "Wray Buntine",

note = "Publisher Copyright: {\textcopyright} Proceedings of the 6th International Workshop on Machine Learning, ICML 1989.; International Workshop on Machine Learning 1989, ICML 1989 ; Conference date: 26-06-1989 Through 27-06-1989",

year = "1989",

doi = "10.1016/B978-1-55860-036-2.50033-3",

language = "English",

pages = "94--98",

editor = "Segre, \{Alberto Maria\}",

booktitle = "Proceedings of the 6th International Workshop on Machine Learning, ICML 1989",

publisher = "Morgan Kaufmann Publishers",

address = "United States of America",

url = "https://www.sciencedirect.com/book/edited-volume/9781558600362/proceedings-of-the-sixth-international-workshop-on-machine-learning",

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PY - 1989

Y1 - 1989

N2 - This paper considers the problem of learning classification rules from data in the context of knowledge acquisition. Bayesian theory provides a framework for both designing learning algorithms and for approaching specific learning applications, for instance, in the selection and tuning of learning tools. Experiments are reported demonstrating how this can be done using two well known learning approaches: simple Bayes classifiers and decision trees.

AB - This paper considers the problem of learning classification rules from data in the context of knowledge acquisition. Bayesian theory provides a framework for both designing learning algorithms and for approaching specific learning applications, for instance, in the selection and tuning of learning tools. Experiments are reported demonstrating how this can be done using two well known learning approaches: simple Bayes classifiers and decision trees.

UR - https://www.scopus.com/pages/publications/85152635261

U2 - 10.1016/B978-1-55860-036-2.50033-3

DO - 10.1016/B978-1-55860-036-2.50033-3

M3 - Conference Paper

AN - SCOPUS:85152635261

SP - 94

EP - 98

BT - Proceedings of the 6th International Workshop on Machine Learning, ICML 1989

A2 - Segre, Alberto Maria

PB - Morgan Kaufmann Publishers

T2 - International Workshop on Machine Learning 1989

Y2 - 26 June 1989 through 27 June 1989

ER -

LEARNING CLASSIFICATION RULES USING BAYES

Abstract

Conference

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