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Glossary of Terms

Glossary of Terms Machine Learning, 30, 271–274 (1998) ° c 1998 Kluwer Academic Publishers, Boston. Manufactured in The Netherlands. To help readers understand common terms in machine learning, statistics, and data mining, we provide a glossary of common terms. The definitions are not designed to be completely general, but instead are aimed at the most common case. Accuracy (error rate) The rate of correct (incorrect) predictions made by the model over a data set (cf. coverage). Accuracy is usually estimated by using an independent test set that was not used at any time during the learning process. More complex accuracy estimation techniques, such as cross-validation and the bootstrap, are commonly used, especially with data sets containing a small number of instances. Association learning Techniques that find conjunctive implication rules of the form “X and Y ! A and B” (associations) that satisfy given criteria. The conventional association algorithms are sound and complete methods for finding all associations that satisfy criteria for minimum support (at least a specified fraction of the instances must satisfy both sides of the rule) and minimum confidence (at least a specified fraction of instances satisfying the left hand side, or antecedent, must satisfy the right hand side, or http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Machine Learning Springer Journals

Glossary of Terms

Machine Learning , Volume 30 (3) – Sep 30, 2004

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References (257)

Publisher
Springer Journals
Copyright
Copyright © 1998 by Kluwer Academic Publishers
Subject
Computer Science; Artificial Intelligence (incl. Robotics); Control, Robotics, Mechatronics; Computing Methodologies; Simulation and Modeling; Language Translation and Linguistics
ISSN
0885-6125
eISSN
1573-0565
DOI
10.1023/A:1017181826899
Publisher site
See Article on Publisher Site

Abstract

Machine Learning, 30, 271–274 (1998) ° c 1998 Kluwer Academic Publishers, Boston. Manufactured in The Netherlands. To help readers understand common terms in machine learning, statistics, and data mining, we provide a glossary of common terms. The definitions are not designed to be completely general, but instead are aimed at the most common case. Accuracy (error rate) The rate of correct (incorrect) predictions made by the model over a data set (cf. coverage). Accuracy is usually estimated by using an independent test set that was not used at any time during the learning process. More complex accuracy estimation techniques, such as cross-validation and the bootstrap, are commonly used, especially with data sets containing a small number of instances. Association learning Techniques that find conjunctive implication rules of the form “X and Y ! A and B” (associations) that satisfy given criteria. The conventional association algorithms are sound and complete methods for finding all associations that satisfy criteria for minimum support (at least a specified fraction of the instances must satisfy both sides of the rule) and minimum confidence (at least a specified fraction of instances satisfying the left hand side, or antecedent, must satisfy the right hand side, or

Journal

Machine LearningSpringer Journals

Published: Sep 30, 2004

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