Predicting Structured Objects with Support Vector Machines.
Machine Learning today offers a broad repertoire of methods for classification and regression. But what if we need to predict complex objects like trees, orderings, or alignments? Such problems arise naturally in natural language processing, search engines, and bioinformatics. The following explores...
| Publicado en: | Communications of the ACM Vol. 52; no. 11; pp. 97 - 105 |
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| Autores principales: | , , , |
| Formato: | Artículo |
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Association for Computing Machinery
Nov2009
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=45021163&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 45021163 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00010782 ACM jtl: Communications of the ACM issn: 00010782 maglogo: N pubinfo: dt: Nov2009 vid: 52 iid: 11 pid: 68 pub: Association for Computing Machinery artinfo: ui: 45021163 10.1145/1592761.1592783 ppf: 97 ppct: 8 formats: tig: atl: Predicting Structured Objects with Support Vector Machines. aug: au: Joachims, Thorsten Hofmann, Thomas Yisong Yue Chun-Nam Yu affil: Department of Computer Science, Cornell University, Ithaca, NY. Google Inc., Zürich, Switzerland. su: Support vector machines Machine learning Prediction models Natural language processing Algorithms Search engine programming sug: subj: Support vector machines Machine learning Prediction models Natural language processing Algorithms Search engine programming ab: Machine Learning today offers a broad repertoire of methods for classification and regression. But what if we need to predict complex objects like trees, orderings, or alignments? Such problems arise naturally in natural language processing, search engines, and bioinformatics. The following explores a generalization of Support Vector Machines (SVMs) for such complex prediction problems. pubtype: Periodical doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2009 holdings: @attributes: islocal: N |
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