Matches in SemOpenAlex for { <https://semopenalex.org/work/W2000930721> ?p ?o ?g. }
- W2000930721 endingPage "2202" @default.
- W2000930721 startingPage "2184" @default.
- W2000930721 abstract "The work proposed in this paper is motivated by the need to develop powerful models and approaches to classify and learn proportional data. Indeed, an abundance of interesting data in several applications occur naturally in this form. Our goal is to discover and capture the intrinsic nature of the data by proposing some approaches that combine the major advantages of generative models namely finite mixtures and discriminative techniques namely support vector machines (SVMs). Indeed, SVMs often rely on classic kernels which are not generally meaningful for proportional data. One serious limitation of these kernels is that they do not take into account the nature of data to classify and choosing a suitable kernel continues to be a formidable challenge for data mining and machine learning researchers. Our approach builds on selecting accurate kernels generated from finite mixtures of Dirichlet, generalized Dirichlet and Beta-Liouville distributions which chief advantage is their flexibility and explanatory capabilities in the case of heterogenous proportional data. Using extensive simulations and a number of experiments involving scene modeling and classification, and automatic image orientation detection, we show the merits of the proposed mixture models and the accuracy of the generated kernels." @default.
- W2000930721 created "2016-06-24" @default.
- W2000930721 creator A5090600716 @default.
- W2000930721 date "2012-12-01" @default.
- W2000930721 modified "2023-10-10" @default.
- W2000930721 title "Hybrid Generative/Discriminative Approaches for Proportional Data Modeling and Classification" @default.
- W2000930721 cites W1501760102 @default.
- W2000930721 cites W1566135517 @default.
- W2000930721 cites W1846690939 @default.
- W2000930721 cites W1885099474 @default.
- W2000930721 cites W1944847058 @default.
- W2000930721 cites W1966829807 @default.
- W2000930721 cites W1969602766 @default.
- W2000930721 cites W1979288205 @default.
- W2000930721 cites W2002123483 @default.
- W2000930721 cites W2015245929 @default.
- W2000930721 cites W2034230784 @default.
- W2000930721 cites W2039957589 @default.
- W2000930721 cites W2050627451 @default.
- W2000930721 cites W2055325763 @default.
- W2000930721 cites W2080529918 @default.
- W2000930721 cites W2094882095 @default.
- W2000930721 cites W2095737716 @default.
- W2000930721 cites W2095940000 @default.
- W2000930721 cites W2096422142 @default.
- W2000930721 cites W2096784803 @default.
- W2000930721 cites W2106848050 @default.
- W2000930721 cites W2107034620 @default.
- W2000930721 cites W2110119381 @default.
- W2000930721 cites W2112427261 @default.
- W2000930721 cites W2114125949 @default.
- W2000930721 cites W2115973703 @default.
- W2000930721 cites W2116064645 @default.
- W2000930721 cites W2117489663 @default.
- W2000930721 cites W2121774207 @default.
- W2000930721 cites W2122762031 @default.
- W2000930721 cites W2124716447 @default.
- W2000930721 cites W2125574651 @default.
- W2000930721 cites W2133014420 @default.
- W2000930721 cites W2134731454 @default.
- W2000930721 cites W2139212933 @default.
- W2000930721 cites W2145441622 @default.
- W2000930721 cites W2146142293 @default.
- W2000930721 cites W2147238549 @default.
- W2000930721 cites W2150400482 @default.
- W2000930721 cites W2151103935 @default.
- W2000930721 cites W2152594362 @default.
- W2000930721 cites W2157487910 @default.
- W2000930721 cites W2159963587 @default.
- W2000930721 cites W2162685317 @default.
- W2000930721 cites W2162915993 @default.
- W2000930721 cites W2169714360 @default.
- W2000930721 cites W2171272633 @default.
- W2000930721 cites W2172246201 @default.
- W2000930721 cites W2488678869 @default.
- W2000930721 cites W4244190911 @default.
- W2000930721 cites W4299542564 @default.
- W2000930721 cites W4300246981 @default.
- W2000930721 doi "https://doi.org/10.1109/tkde.2011.162" @default.
- W2000930721 hasPublicationYear "2012" @default.
- W2000930721 type Work @default.
- W2000930721 sameAs 2000930721 @default.
- W2000930721 citedByCount "77" @default.
- W2000930721 countsByYear W20009307212012 @default.
- W2000930721 countsByYear W20009307212013 @default.
- W2000930721 countsByYear W20009307212014 @default.
- W2000930721 countsByYear W20009307212015 @default.
- W2000930721 countsByYear W20009307212016 @default.
- W2000930721 countsByYear W20009307212017 @default.
- W2000930721 countsByYear W20009307212018 @default.
- W2000930721 countsByYear W20009307212019 @default.
- W2000930721 countsByYear W20009307212020 @default.
- W2000930721 countsByYear W20009307212021 @default.
- W2000930721 countsByYear W20009307212022 @default.
- W2000930721 countsByYear W20009307212023 @default.
- W2000930721 crossrefType "journal-article" @default.
- W2000930721 hasAuthorship W2000930721A5090600716 @default.
- W2000930721 hasConcept C105795698 @default.
- W2000930721 hasConcept C114614502 @default.
- W2000930721 hasConcept C119857082 @default.
- W2000930721 hasConcept C122280245 @default.
- W2000930721 hasConcept C12267149 @default.
- W2000930721 hasConcept C124101348 @default.
- W2000930721 hasConcept C134306372 @default.
- W2000930721 hasConcept C153180895 @default.
- W2000930721 hasConcept C154945302 @default.
- W2000930721 hasConcept C167966045 @default.
- W2000930721 hasConcept C169214877 @default.
- W2000930721 hasConcept C171686336 @default.
- W2000930721 hasConcept C182310444 @default.
- W2000930721 hasConcept C2780598303 @default.
- W2000930721 hasConcept C33923547 @default.
- W2000930721 hasConcept C39890363 @default.
- W2000930721 hasConcept C41008148 @default.
- W2000930721 hasConcept C500882744 @default.
- W2000930721 hasConcept C67186912 @default.
- W2000930721 hasConcept C74193536 @default.
- W2000930721 hasConcept C77088390 @default.