Neighborhood Property-Based Pattern Selection for Support Vector Machines.
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| Title: | Neighborhood Property-Based Pattern Selection for Support Vector Machines. |
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| Authors: | Hyunjung Shin1,2 shin@ajou.ac.kr, Sungzoon Cho3 zoon@snu.ac.kr |
| Source: | Neural Computation. Mar2007, Vol. 19 Issue 3, p816-855. 40p. |
| Subjects: | Vector analysis, Selection theorems, Combinatorial set theory, Algorithms, Universal algebra, Complex numbers, Mathematics |
| Abstract: | The support vector machine (SVM) has been spotlighted in the machine learning community because of its theoretical soundness and practical performance. When applied to a large data set, however, it requires a large memory and a long time for training. To cope with the practical difficulty, we propose a pattern selection algorithm based on neighborhood properties. The idea is to select only the patterns that are likely to be located near the decision boundary. Those patterns are expected to be more informative than the randomly selected patterns. The experimental results provide promising evidence that it is possible to successfully employ the proposed algorithm ahead of SVM training. [ABSTRACT FROM AUTHOR] |
| Copyright of Neural Computation is the property of MIT Press 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: 24378246 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Neighborhood Property-Based Pattern Selection for Support Vector Machines. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Hyunjung+Shin%22">Hyunjung Shin</searchLink><relatesTo>1,2</relatesTo><i> shin@ajou.ac.kr</i><br /><searchLink fieldCode="AR" term="%22Sungzoon+Cho%22">Sungzoon Cho</searchLink><relatesTo>3</relatesTo><i> zoon@snu.ac.kr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Neural+Computation%22">Neural Computation</searchLink>. Mar2007, Vol. 19 Issue 3, p816-855. 40p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Vector+analysis%22">Vector analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Selection+theorems%22">Selection theorems</searchLink><br /><searchLink fieldCode="DE" term="%22Combinatorial+set+theory%22">Combinatorial set theory</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Universal+algebra%22">Universal algebra</searchLink><br /><searchLink fieldCode="DE" term="%22Complex+numbers%22">Complex numbers</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematics%22">Mathematics</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The support vector machine (SVM) has been spotlighted in the machine learning community because of its theoretical soundness and practical performance. When applied to a large data set, however, it requires a large memory and a long time for training. To cope with the practical difficulty, we propose a pattern selection algorithm based on neighborhood properties. The idea is to select only the patterns that are likely to be located near the decision boundary. Those patterns are expected to be more informative than the randomly selected patterns. The experimental results provide promising evidence that it is possible to successfully employ the proposed algorithm ahead of SVM training. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Neural Computation is the property of MIT Press 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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1162/neco.2007.19.3.816 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 40 StartPage: 816 Subjects: – SubjectFull: Vector analysis Type: general – SubjectFull: Selection theorems Type: general – SubjectFull: Combinatorial set theory Type: general – SubjectFull: Algorithms Type: general – SubjectFull: Universal algebra Type: general – SubjectFull: Complex numbers Type: general – SubjectFull: Mathematics Type: general Titles: – TitleFull: Neighborhood Property-Based Pattern Selection for Support Vector Machines. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Hyunjung Shin – PersonEntity: Name: NameFull: Sungzoon Cho IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2007 Type: published Y: 2007 Identifiers: – Type: issn-print Value: 08997667 Numbering: – Type: volume Value: 19 – Type: issue Value: 3 Titles: – TitleFull: Neural Computation Type: main |
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