| Nonlinear knowledge in kernel approximation. | |
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MedLine Citation:
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PMID: 17278481 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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Prior knowledge over arbitrary general sets is incorporated into nonlinear kernel approximation problems in the form of linear constraints in a linear program. The key tool in this incorporation is a theorem of the alternative for convex functions that converts nonlinear prior knowledge implications into linear inequalities without the need to kernelize these implications. Effectiveness of the proposed formulation is demonstrated on two synthetic examples and an important lymph node metastasis prediction problem. All these problems exhibit marked improvements upon the introduction of prior knowledge over nonlinear kernel approximation approaches that do not utilize such knowledge. |
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Authors:
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O L Mangasarian; E W Wild |
Publication Detail:
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Type: Letter; Research Support, U.S. Gov't, Non-P.H.S. |
Journal Detail:
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Title: IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council Volume: 18 ISSN: 1045-9227 ISO Abbreviation: - Publication Date: 2007 Jan |
Date Detail:
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Created Date: 2007-02-06 Completed Date: 2007-02-28 Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 101211035 Medline TA: IEEE Trans Neural Netw Country: United States |
Other Details:
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Languages: eng Pagination: 300-6 Citation Subset: IM |
Export Citation:
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APA/MLA Format Download EndNote Download BibTex |
| MeSH Terms | |
Descriptor/Qualifier:
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Artificial Intelligence* Breast Neoplasms / pathology*, secondary* Diagnosis, Computer-Assisted / methods* Female Humans Lymphatic Metastasis Nonlinear Dynamics Pattern Recognition, Automated / methods* Prognosis Reproducibility of Results Risk Assessment / methods* Risk Factors Sensitivity and Specificity |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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