Search-based structured prediction.
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| Title: | Search-based structured prediction. |
|---|---|
| Authors: | Daumé, Hal1 me@hal3.name, Langford, John2, Marcu, Daniel3 |
| Source: | Machine Learning. Jun2009, Vol. 75 Issue 3, p297-325. 29p. 3 Diagrams, 5 Charts, 1 Graph. |
| Subjects: | Forecasting, Structured techniques of electronic data processing, Prediction models, Algorithms, Classification, Mathematical decomposition |
| Abstract: | We present Searn, an algorithm for integrating search and l earning to solve complex structured prediction problems such as those that occur in natural language, speech, computational biology, and vision. Searn is a meta-algorithm that transforms these complex problems into simple classification problems to which any binary classifier may be applied. Unlike current algorithms for structured learning that require decomposition of both the loss function and the feature functions over the predicted structure, Searn is able to learn prediction functions for any loss function and any class of features. Moreover, Searn comes with a strong, natural theoretical guarantee: good performance on the derived classification problems implies good performance on the structured prediction problem. [ABSTRACT FROM AUTHOR] |
| Copyright of Machine Learning is the property of Springer Nature 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: 39563914 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Search-based structured prediction. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Daumé%2C+Hal%22">Daumé, Hal</searchLink><relatesTo>1</relatesTo><i> me@hal3.name</i><br /><searchLink fieldCode="AR" term="%22Langford%2C+John%22">Langford, John</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Marcu%2C+Daniel%22">Marcu, Daniel</searchLink><relatesTo>3</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Machine+Learning%22">Machine Learning</searchLink>. Jun2009, Vol. 75 Issue 3, p297-325. 29p. 3 Diagrams, 5 Charts, 1 Graph. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Structured+techniques+of+electronic+data+processing%22">Structured techniques of electronic data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Classification%22">Classification</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+decomposition%22">Mathematical decomposition</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: We present Searn, an algorithm for integrating search and l earning to solve complex structured prediction problems such as those that occur in natural language, speech, computational biology, and vision. Searn is a meta-algorithm that transforms these complex problems into simple classification problems to which any binary classifier may be applied. Unlike current algorithms for structured learning that require decomposition of both the loss function and the feature functions over the predicted structure, Searn is able to learn prediction functions for any loss function and any class of features. Moreover, Searn comes with a strong, natural theoretical guarantee: good performance on the derived classification problems implies good performance on the structured prediction problem. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Machine Learning is the property of Springer Nature 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=39563914 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10994-009-5106-x Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 29 StartPage: 297 Subjects: – SubjectFull: Forecasting Type: general – SubjectFull: Structured techniques of electronic data processing Type: general – SubjectFull: Prediction models Type: general – SubjectFull: Algorithms Type: general – SubjectFull: Classification Type: general – SubjectFull: Mathematical decomposition Type: general Titles: – TitleFull: Search-based structured prediction. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Daumé, Hal – PersonEntity: Name: NameFull: Langford, John – PersonEntity: Name: NameFull: Marcu, Daniel IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2009 Type: published Y: 2009 Identifiers: – Type: issn-print Value: 08856125 Numbering: – Type: volume Value: 75 – Type: issue Value: 3 Titles: – TitleFull: Machine Learning Type: main |
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