Matches in SemOpenAlex for { <https://semopenalex.org/work/W65546612> ?p ?o ?g. }
Showing items 1 to 64 of
64
with 100 items per page.
- W65546612 endingPage "274" @default.
- W65546612 startingPage "265" @default.
- W65546612 abstract "A major drawback of most existing medical decision support systems is that they do not provide any indication about the uncertainty of each of their predictions. This paper addresses this problem with the use of a new machine learning framework for producing valid probabilistic predictions, called Venn Prediction (VP). More specifically, VP is combined with Neural Networks (NNs), which is one of the most widely used machine learning algorithms. The obtained experimental results on two medical datasets demonstrate empirically the validity of the VP outputs and their superiority over the outputs of the original NN classifier in terms of reliability." @default.
- W65546612 created "2016-06-24" @default.
- W65546612 creator A5041663459 @default.
- W65546612 date "2011-01-01" @default.
- W65546612 modified "2023-09-26" @default.
- W65546612 title "Reliable Probabilistic Prediction for Medical Decision Support" @default.
- W65546612 cites W1485956525 @default.
- W65546612 cites W1499689786 @default.
- W65546612 cites W1541848738 @default.
- W65546612 cites W1594344279 @default.
- W65546612 cites W1652955026 @default.
- W65546612 cites W1991426267 @default.
- W65546612 cites W1995396954 @default.
- W65546612 cites W2010546252 @default.
- W65546612 cites W2037959834 @default.
- W65546612 cites W2047634553 @default.
- W65546612 cites W2083780116 @default.
- W65546612 cites W2093126354 @default.
- W65546612 cites W2098075945 @default.
- W65546612 cites W2130211925 @default.
- W65546612 doi "https://doi.org/10.1007/978-3-642-23960-1_32" @default.
- W65546612 hasPublicationYear "2011" @default.
- W65546612 type Work @default.
- W65546612 sameAs 65546612 @default.
- W65546612 citedByCount "15" @default.
- W65546612 countsByYear W655466122012 @default.
- W65546612 countsByYear W655466122013 @default.
- W65546612 countsByYear W655466122014 @default.
- W65546612 countsByYear W655466122015 @default.
- W65546612 countsByYear W655466122016 @default.
- W65546612 countsByYear W655466122017 @default.
- W65546612 countsByYear W655466122021 @default.
- W65546612 crossrefType "book-chapter" @default.
- W65546612 hasAuthorship W65546612A5041663459 @default.
- W65546612 hasBestOaLocation W655466121 @default.
- W65546612 hasConcept C119857082 @default.
- W65546612 hasConcept C154945302 @default.
- W65546612 hasConcept C41008148 @default.
- W65546612 hasConcept C49937458 @default.
- W65546612 hasConceptScore W65546612C119857082 @default.
- W65546612 hasConceptScore W65546612C154945302 @default.
- W65546612 hasConceptScore W65546612C41008148 @default.
- W65546612 hasConceptScore W65546612C49937458 @default.
- W65546612 hasLocation W655466121 @default.
- W65546612 hasLocation W655466122 @default.
- W65546612 hasLocation W655466123 @default.
- W65546612 hasOpenAccess W65546612 @default.
- W65546612 hasPrimaryLocation W655466121 @default.
- W65546612 hasRelatedWork W1575659177 @default.
- W65546612 hasRelatedWork W2961085424 @default.
- W65546612 hasRelatedWork W3046775127 @default.
- W65546612 hasRelatedWork W3107474891 @default.
- W65546612 hasRelatedWork W4205958290 @default.
- W65546612 hasRelatedWork W4286629047 @default.
- W65546612 hasRelatedWork W4290792893 @default.
- W65546612 hasRelatedWork W4306321456 @default.
- W65546612 hasRelatedWork W4306674287 @default.
- W65546612 hasRelatedWork W4224009465 @default.
- W65546612 isParatext "false" @default.
- W65546612 isRetracted "false" @default.
- W65546612 magId "65546612" @default.
- W65546612 workType "book-chapter" @default.