FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2103403883> ?p ?o ?g. }

• W2103403883 abstract "Abstract Background Overfitting the data is a salient issue for classifier design in small-sample settings. This is why selecting a classifier from a constrained family of classifiers, ones that do not possess the potential to too finely partition the feature space, is typically preferable. But overfitting is not merely a consequence of the classifier family; it is highly dependent on the classification rule used to design a classifier from the sample data. Thus, it is possible to consider families that are rather complex but for which there are classification rules that perform well for small samples. Such classification rules can be advantageous because they facilitate satisfactory classification when the class-conditional distributions are not easily separated and the sample is not large. Here we consider neural networks, from the perspectives of classical design based solely on the sample data and from noise-injection-based design. Results This paper provides an extensive simulation-based comparative study of noise-injected neural-network design. It considers a number of different feature-label models across various small sample sizes using varying amounts of noise injection. Besides comparing noise-injected neural-network design to classical neural-network design, the paper compares it to a number of other classification rules. Our particular interest is with the use of microarray data for expression-based classification for diagnosis and prognosis. To that end, we consider noise-injected neural-network design as it relates to a study of survivability of breast cancer patients. Conclusion The conclusion is that in many instances noise-injected neural network design is superior to the other tested methods, and in almost all cases it does not perform substantially worse than the best of the other methods. Since the amount of noise injected is consequential, the effect of differing amounts of injected noise must be considered." @default.
• W2103403883 created "2016-06-24" @default.
• W2103403883 creator A5010097254 @default.
• W2103403883 creator A5040756848 @default.
• W2103403883 creator A5048587276 @default.
• W2103403883 creator A5058154161 @default.
• W2103403883 date "2006-05-31" @default.
• W2103403883 modified "2023-10-08" @default.
• W2103403883 title "Noise-injected neural networks show promise for use on small-sample expression data" @default.
• W2103403883 cites W1564947197 @default.
• W2103403883 cites W1964929969 @default.
• W2103403883 cites W2032581050 @default.
• W2103403883 cites W2051265608 @default.
• W2103403883 cites W2057853719 @default.
• W2103403883 cites W2070665556 @default.
• W2103403883 cites W2098874108 @default.
• W2103403883 cites W2109363337 @default.
• W2103403883 cites W2110298216 @default.
• W2103403883 cites W2111406701 @default.
• W2103403883 cites W2114625253 @default.
• W2103403883 cites W2124136621 @default.
• W2103403883 cites W2125056473 @default.
• W2103403883 cites W2128985829 @default.
• W2103403883 cites W2135999858 @default.
• W2103403883 cites W2136487516 @default.
• W2103403883 cites W2160450758 @default.
• W2103403883 cites W2160687178 @default.
• W2103403883 cites W2166688876 @default.
• W2103403883 cites W2169359910 @default.
• W2103403883 cites W4238284510 @default.
• W2103403883 cites W81216941 @default.
• W2103403883 doi "https://doi.org/10.1186/1471-2105-7-274" @default.
• W2103403883 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/1524820" @default.
• W2103403883 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/16737545" @default.
• W2103403883 hasPublicationYear "2006" @default.
• W2103403883 type Work @default.
• W2103403883 sameAs 2103403883 @default.
• W2103403883 citedByCount "13" @default.
• W2103403883 countsByYear W21034038832012 @default.
• W2103403883 countsByYear W21034038832013 @default.
• W2103403883 countsByYear W21034038832014 @default.
• W2103403883 countsByYear W21034038832015 @default.
• W2103403883 countsByYear W21034038832018 @default.
• W2103403883 countsByYear W21034038832019 @default.
• W2103403883 countsByYear W21034038832021 @default.
• W2103403883 countsByYear W21034038832022 @default.
• W2103403883 crossrefType "journal-article" @default.
• W2103403883 hasAuthorship W2103403883A5010097254 @default.
• W2103403883 hasAuthorship W2103403883A5040756848 @default.
• W2103403883 hasAuthorship W2103403883A5048587276 @default.
• W2103403883 hasAuthorship W2103403883A5058154161 @default.
• W2103403883 hasBestOaLocation W21034038831 @default.
• W2103403883 hasConcept C105795698 @default.
• W2103403883 hasConcept C115961682 @default.
• W2103403883 hasConcept C119857082 @default.
• W2103403883 hasConcept C124101348 @default.
• W2103403883 hasConcept C129848803 @default.
• W2103403883 hasConcept C153180895 @default.
• W2103403883 hasConcept C154945302 @default.
• W2103403883 hasConcept C22019652 @default.
• W2103403883 hasConcept C33923547 @default.
• W2103403883 hasConcept C41008148 @default.
• W2103403883 hasConcept C50644808 @default.
• W2103403883 hasConcept C95623464 @default.
• W2103403883 hasConcept C99498987 @default.
• W2103403883 hasConceptScore W2103403883C105795698 @default.
• W2103403883 hasConceptScore W2103403883C115961682 @default.
• W2103403883 hasConceptScore W2103403883C119857082 @default.
• W2103403883 hasConceptScore W2103403883C124101348 @default.
• W2103403883 hasConceptScore W2103403883C129848803 @default.
• W2103403883 hasConceptScore W2103403883C153180895 @default.
• W2103403883 hasConceptScore W2103403883C154945302 @default.
• W2103403883 hasConceptScore W2103403883C22019652 @default.
• W2103403883 hasConceptScore W2103403883C33923547 @default.
• W2103403883 hasConceptScore W2103403883C41008148 @default.
• W2103403883 hasConceptScore W2103403883C50644808 @default.
• W2103403883 hasConceptScore W2103403883C95623464 @default.
• W2103403883 hasConceptScore W2103403883C99498987 @default.
• W2103403883 hasIssue "1" @default.
• W2103403883 hasLocation W21034038831 @default.
• W2103403883 hasLocation W21034038832 @default.
• W2103403883 hasLocation W21034038833 @default.
• W2103403883 hasLocation W21034038834 @default.
• W2103403883 hasLocation W21034038835 @default.
• W2103403883 hasLocation W21034038836 @default.
• W2103403883 hasOpenAccess W2103403883 @default.
• W2103403883 hasPrimaryLocation W21034038831 @default.
• W2103403883 hasRelatedWork W1996541855 @default.
• W2103403883 hasRelatedWork W2563096758 @default.
• W2103403883 hasRelatedWork W2742991909 @default.
• W2103403883 hasRelatedWork W2767651786 @default.
• W2103403883 hasRelatedWork W2940336242 @default.
• W2103403883 hasRelatedWork W2989932438 @default.
• W2103403883 hasRelatedWork W3099765033 @default.
• W2103403883 hasRelatedWork W4210794429 @default.
• W2103403883 hasRelatedWork W4283732135 @default.
• W2103403883 hasRelatedWork W4313159793 @default.
• W2103403883 hasVolume "7" @default.
• W2103403883 isParatext "false" @default.
• W2103403883 isRetracted "false" @default.

• next