Integrating prior domain knowledge into discriminative learning using automatic model construction and phantom examples
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| Title: | Integrating prior domain knowledge into discriminative learning using automatic model construction and phantom examples |
|---|---|
| Authors: | Lim, Shiau Hong shonglim@illinois.edu, Wang, Li-Lun1, DeJong, Gerald1 |
| Source: | Pattern Recognition. Dec2009, Vol. 42 Issue 12, p3231-3240. 10p. |
| Subjects: | Machine learning, Chinese character sets (Data processing), Pattern perception, Robust control, Computer science |
| Abstract: | Abstract: Domain knowledge captures an expert''s approximate understanding of the world, its objects, and their properties. When available, it should serve to augment the information in a classification learner''s training set. But this form of prior knowledge does not easily fit into the statistical learning paradigm. We propose and evaluate the use of phantom examples to remedy this. Our system performs automated model construction and learns generative models for phantom examples that adapt to the need of individual tasks. The approach is validated on the challenging real-world task of distinguishing handwritten Chinese characters. The approach improves learning significantly, provides additional robustness, and works well even though the domain knowledge is imperfect and approximate. [Copyright &y& Elsevier] |
| Copyright of Pattern Recognition is the property of Pergamon Press - An Imprint of Elsevier Science 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 | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 43766391 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Integrating prior domain knowledge into discriminative learning using automatic model construction and phantom examples – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lim%2C+Shiau+Hong%22">Lim, Shiau Hong</searchLink><i> shonglim@illinois.edu</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Li-Lun%22">Wang, Li-Lun</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22DeJong%2C+Gerald%22">DeJong, Gerald</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Pattern+Recognition%22">Pattern Recognition</searchLink>. Dec2009, Vol. 42 Issue 12, p3231-3240. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Chinese+character+sets+%28Data+processing%29%22">Chinese character sets (Data processing)</searchLink><br /><searchLink fieldCode="DE" term="%22Pattern+perception%22">Pattern perception</searchLink><br /><searchLink fieldCode="DE" term="%22Robust+control%22">Robust control</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+science%22">Computer science</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Abstract: Domain knowledge captures an expert''s approximate understanding of the world, its objects, and their properties. When available, it should serve to augment the information in a classification learner''s training set. But this form of prior knowledge does not easily fit into the statistical learning paradigm. We propose and evaluate the use of phantom examples to remedy this. Our system performs automated model construction and learns generative models for phantom examples that adapt to the need of individual tasks. The approach is validated on the challenging real-world task of distinguishing handwritten Chinese characters. The approach improves learning significantly, provides additional robustness, and works well even though the domain knowledge is imperfect and approximate. [Copyright &y& Elsevier] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Pattern Recognition is the property of Pergamon Press - An Imprint of Elsevier Science 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=43766391 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.patcog.2008.12.012 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 3231 Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Chinese character sets (Data processing) Type: general – SubjectFull: Pattern perception Type: general – SubjectFull: Robust control Type: general – SubjectFull: Computer science Type: general Titles: – TitleFull: Integrating prior domain knowledge into discriminative learning using automatic model construction and phantom examples Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lim, Shiau Hong – PersonEntity: Name: NameFull: Wang, Li-Lun – PersonEntity: Name: NameFull: DeJong, Gerald IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2009 Type: published Y: 2009 Identifiers: – Type: issn-print Value: 00313203 Numbering: – Type: volume Value: 42 – Type: issue Value: 12 Titles: – TitleFull: Pattern Recognition Type: main |
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