Learning with Decision Lists of Data-Dependent Features.
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| Title: | Learning with Decision Lists of Data-Dependent Features. |
|---|---|
| Authors: | Marchand, Mario1 MARIO.MARCHAND@IFT.ULAVAL.CA, Sokolova, Marina2 SOKOLOVA@SITE.UOTTAWA.CA, Warmuth, Manfred K. |
| Source: | Journal of Machine Learning Research. 4/1/2005, Vol. 6 Issue 4, p427-451. 25p. 6 Charts. |
| Subjects: | Mathematical models of learning, Stochastic learning models, Sequential analysis, Decision logic tables, Algorithms |
| Abstract: | We present a learning algorithm for decision lists which allows features that are constructed from the data and allows a trade-off between accuracy and complexity. We provide bounds on the generalization error of this learning algorithm in terms of the number of errors and the size of the classifier it finds on the training data. We also compare its performance on some natural data sets with the set covering machine and the support vector machine. Furthermore, we show that the proposed bounds on the generalization error provide effective guides for model selection. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Machine Learning Research is the property of Microtome Publishing and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 18003318 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Learning with Decision Lists of Data-Dependent Features. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Marchand%2C+Mario%22">Marchand, Mario</searchLink><relatesTo>1</relatesTo><i> MARIO.MARCHAND@IFT.ULAVAL.CA</i><br /><searchLink fieldCode="AR" term="%22Sokolova%2C+Marina%22">Sokolova, Marina</searchLink><relatesTo>2</relatesTo><i> SOKOLOVA@SITE.UOTTAWA.CA</i><br /><searchLink fieldCode="AR" term="%22Warmuth%2C+Manfred+K%2E%22">Warmuth, Manfred K.</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Machine+Learning+Research%22">Journal of Machine Learning Research</searchLink>. 4/1/2005, Vol. 6 Issue 4, p427-451. 25p. 6 Charts. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Mathematical+models+of+learning%22">Mathematical models of learning</searchLink><br /><searchLink fieldCode="DE" term="%22Stochastic+learning+models%22">Stochastic learning models</searchLink><br /><searchLink fieldCode="DE" term="%22Sequential+analysis%22">Sequential analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+logic+tables%22">Decision logic tables</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: We present a learning algorithm for decision lists which allows features that are constructed from the data and allows a trade-off between accuracy and complexity. We provide bounds on the generalization error of this learning algorithm in terms of the number of errors and the size of the classifier it finds on the training data. We also compare its performance on some natural data sets with the set covering machine and the support vector machine. Furthermore, we show that the proposed bounds on the generalization error provide effective guides for model selection. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Machine Learning Research is the property of Microtome Publishing and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=18003318 |
| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 25 StartPage: 427 Subjects: – SubjectFull: Mathematical models of learning Type: general – SubjectFull: Stochastic learning models Type: general – SubjectFull: Sequential analysis Type: general – SubjectFull: Decision logic tables Type: general – SubjectFull: Algorithms Type: general Titles: – TitleFull: Learning with Decision Lists of Data-Dependent Features. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Marchand, Mario – PersonEntity: Name: NameFull: Sokolova, Marina – PersonEntity: Name: NameFull: Warmuth, Manfred K. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: 4/1/2005 Type: published Y: 2005 Identifiers: – Type: issn-print Value: 15324435 Numbering: – Type: volume Value: 6 – Type: issue Value: 4 Titles: – TitleFull: Journal of Machine Learning Research Type: main |
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