Matches in SemOpenAlex for { <https://semopenalex.org/work/W2003048487> ?p ?o ?g. }
Showing items 1 to 91 of
91
with 100 items per page.
- W2003048487 endingPage "78" @default.
- W2003048487 startingPage "57" @default.
- W2003048487 abstract "In the past, nearest neighbor algorithms for learning from examples have worked best in domains in which all features had numeric values. In such domains, the examples can be treated as points and distance metrics can use standard definitions. In symbolic domains, a more sophisticated treatment of the feature space is required. We introduce a nearest neighbor algorithm for learning in domains with symbolic features. Our algorithm calculates distance tables that allow it to produce real-valued distances between instances, and attaches weights to the instances to further modify the structure of feature space. We show that this technique produces excellent classification accuracy on three problems that have been studied by machine learning researchers: predicting protein secondary structure, identifying DNA promoter sequences, and pronouncing English text. Direct experimental comparisons with the other learning algorithms show that our nearest neighbor algorithm is comparable or superior in all three domains. In addition, our algorithm has advantages in training speed, simplicity, and perspicuity. We conclude that experimental evidence favors the use and continued development of nearest neighbor algorithms for domains such as the ones studied here." @default.
- W2003048487 created "2016-06-24" @default.
- W2003048487 creator A5000592886 @default.
- W2003048487 creator A5010384159 @default.
- W2003048487 date "1993-01-01" @default.
- W2003048487 modified "2023-10-11" @default.
- W2003048487 cites W1481628254 @default.
- W2003048487 cites W1498436455 @default.
- W2003048487 cites W1499127866 @default.
- W2003048487 cites W1503632486 @default.
- W2003048487 cites W1505652865 @default.
- W2003048487 cites W1506514354 @default.
- W2003048487 cites W1541342456 @default.
- W2003048487 cites W1542067597 @default.
- W2003048487 cites W1551220402 @default.
- W2003048487 cites W1552785817 @default.
- W2003048487 cites W1559570474 @default.
- W2003048487 cites W1580500061 @default.
- W2003048487 cites W1823011326 @default.
- W2003048487 cites W1979766417 @default.
- W2003048487 cites W2004131797 @default.
- W2003048487 cites W2005314985 @default.
- W2003048487 cites W2008708467 @default.
- W2003048487 cites W2010919933 @default.
- W2003048487 cites W2046827412 @default.
- W2003048487 cites W2057157558 @default.
- W2003048487 cites W2061500593 @default.
- W2003048487 cites W2086618114 @default.
- W2003048487 cites W2095450147 @default.
- W2003048487 cites W2109553965 @default.
- W2003048487 cites W2119423166 @default.
- W2003048487 cites W2122111042 @default.
- W2003048487 cites W2127244329 @default.
- W2003048487 cites W2135479218 @default.
- W2003048487 cites W2141545068 @default.
- W2003048487 cites W2146257637 @default.
- W2003048487 cites W2147169507 @default.
- W2003048487 cites W2189004000 @default.
- W2003048487 cites W2409752219 @default.
- W2003048487 cites W2605974740 @default.
- W2003048487 cites W293526712 @default.
- W2003048487 cites W3207342693 @default.
- W2003048487 cites W48969124 @default.
- W2003048487 cites W88158985 @default.
- W2003048487 cites W2504871398 @default.
- W2003048487 doi "https://doi.org/10.1023/a:1022664626993" @default.
- W2003048487 hasPublicationYear "1993" @default.
- W2003048487 type Work @default.
- W2003048487 sameAs 2003048487 @default.
- W2003048487 citedByCount "373" @default.
- W2003048487 countsByYear W20030484872012 @default.
- W2003048487 countsByYear W20030484872013 @default.
- W2003048487 countsByYear W20030484872014 @default.
- W2003048487 countsByYear W20030484872015 @default.
- W2003048487 countsByYear W20030484872016 @default.
- W2003048487 countsByYear W20030484872017 @default.
- W2003048487 countsByYear W20030484872018 @default.
- W2003048487 countsByYear W20030484872019 @default.
- W2003048487 countsByYear W20030484872020 @default.
- W2003048487 countsByYear W20030484872021 @default.
- W2003048487 crossrefType "journal-article" @default.
- W2003048487 hasAuthorship W2003048487A5000592886 @default.
- W2003048487 hasAuthorship W2003048487A5010384159 @default.
- W2003048487 hasBestOaLocation W20030484871 @default.
- W2003048487 hasConcept C154945302 @default.
- W2003048487 hasConcept C33923547 @default.
- W2003048487 hasConcept C41008148 @default.
- W2003048487 hasConceptScore W2003048487C154945302 @default.
- W2003048487 hasConceptScore W2003048487C33923547 @default.
- W2003048487 hasConceptScore W2003048487C41008148 @default.
- W2003048487 hasIssue "1" @default.
- W2003048487 hasLocation W20030484871 @default.
- W2003048487 hasOpenAccess W2003048487 @default.
- W2003048487 hasPrimaryLocation W20030484871 @default.
- W2003048487 hasRelatedWork W1974891317 @default.
- W2003048487 hasRelatedWork W2007596026 @default.
- W2003048487 hasRelatedWork W2044189972 @default.
- W2003048487 hasRelatedWork W2069964982 @default.
- W2003048487 hasRelatedWork W2313400459 @default.
- W2003048487 hasRelatedWork W2748952813 @default.
- W2003048487 hasRelatedWork W2899084033 @default.
- W2003048487 hasRelatedWork W3107474891 @default.
- W2003048487 hasRelatedWork W4225152035 @default.
- W2003048487 hasRelatedWork W4245490552 @default.
- W2003048487 hasVolume "10" @default.
- W2003048487 isParatext "false" @default.
- W2003048487 isRetracted "false" @default.
- W2003048487 magId "2003048487" @default.
- W2003048487 workType "article" @default.