Document Detail


Nonlinear knowledge in kernel approximation.
MedLine Citation:
PMID:  17278481     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
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.
Authors:
O L Mangasarian; E W Wild
Publication Detail:
Type:  Letter; Research Support, U.S. Gov't, Non-P.H.S.    
Journal Detail:
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:
Created Date:  2007-02-06     Completed Date:  2007-02-28     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101211035     Medline TA:  IEEE Trans Neural Netw     Country:  United States    
Other Details:
Languages:  eng     Pagination:  300-6     Citation Subset:  IM    
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MeSH Terms
Descriptor/Qualifier:
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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