Matches in SemOpenAlex for { <https://semopenalex.org/work/W2145328215> ?p ?o ?g. }
Showing items 1 to 86 of
86
with 100 items per page.
- W2145328215 abstract "This paper introduces a nonlinear logistic regression model for classification. The main idea is to map the data to a feature space based on kernel density estimation. A discriminative model is then learned to optimize the feature weights as well as the bandwidth of a Nadaraya-Watson kernel density estimator. We then propose a hierarchical optimization algorithm for learning the coefficients and kernel bandwidths in an integrated way. Compared to other nonlinear models such as kernel logistic regression (KLR) and SVM, our approach is far more efficient since it solves an optimization problem with a much smaller size. Two other major advantages are that it can cope with categorical attributes in a unified fashion and naturally handle multi-class problems. Moveover, our approach inherits from logistic regression good interpretability of the model, which is important for clinical applications but not offered by KLR and SVM. Extensive results on real datasets, including a clinical prediction application currently under deployment in a major hospital, show that our approach not only achieves superior classification accuracy, but also drastically reduces the computing time as compared to other leading methods." @default.
- W2145328215 created "2016-06-24" @default.
- W2145328215 creator A5037955098 @default.
- W2145328215 creator A5047318884 @default.
- W2145328215 creator A5066193828 @default.
- W2145328215 creator A5069658166 @default.
- W2145328215 date "2013-08-11" @default.
- W2145328215 modified "2023-09-26" @default.
- W2145328215 title "Density-based logistic regression" @default.
- W2145328215 cites W1496317909 @default.
- W2145328215 cites W1990302430 @default.
- W2145328215 cites W2062000081 @default.
- W2145328215 cites W2070272652 @default.
- W2145328215 cites W2106868411 @default.
- W2145328215 cites W2131479089 @default.
- W2145328215 cites W2158001550 @default.
- W2145328215 cites W2541822344 @default.
- W2145328215 cites W2787894218 @default.
- W2145328215 cites W4233014035 @default.
- W2145328215 doi "https://doi.org/10.1145/2487575.2487583" @default.
- W2145328215 hasPublicationYear "2013" @default.
- W2145328215 type Work @default.
- W2145328215 sameAs 2145328215 @default.
- W2145328215 citedByCount "18" @default.
- W2145328215 countsByYear W21453282152014 @default.
- W2145328215 countsByYear W21453282152015 @default.
- W2145328215 countsByYear W21453282152016 @default.
- W2145328215 countsByYear W21453282152017 @default.
- W2145328215 countsByYear W21453282152018 @default.
- W2145328215 countsByYear W21453282152019 @default.
- W2145328215 countsByYear W21453282152020 @default.
- W2145328215 countsByYear W21453282152022 @default.
- W2145328215 crossrefType "proceedings-article" @default.
- W2145328215 hasAuthorship W2145328215A5037955098 @default.
- W2145328215 hasAuthorship W2145328215A5047318884 @default.
- W2145328215 hasAuthorship W2145328215A5066193828 @default.
- W2145328215 hasAuthorship W2145328215A5069658166 @default.
- W2145328215 hasConcept C105795698 @default.
- W2145328215 hasConcept C114614502 @default.
- W2145328215 hasConcept C119857082 @default.
- W2145328215 hasConcept C12267149 @default.
- W2145328215 hasConcept C124101348 @default.
- W2145328215 hasConcept C151956035 @default.
- W2145328215 hasConcept C153180895 @default.
- W2145328215 hasConcept C154945302 @default.
- W2145328215 hasConcept C185429906 @default.
- W2145328215 hasConcept C2781067378 @default.
- W2145328215 hasConcept C33923547 @default.
- W2145328215 hasConcept C41008148 @default.
- W2145328215 hasConcept C5274069 @default.
- W2145328215 hasConcept C71134354 @default.
- W2145328215 hasConcept C74193536 @default.
- W2145328215 hasConcept C97931131 @default.
- W2145328215 hasConceptScore W2145328215C105795698 @default.
- W2145328215 hasConceptScore W2145328215C114614502 @default.
- W2145328215 hasConceptScore W2145328215C119857082 @default.
- W2145328215 hasConceptScore W2145328215C12267149 @default.
- W2145328215 hasConceptScore W2145328215C124101348 @default.
- W2145328215 hasConceptScore W2145328215C151956035 @default.
- W2145328215 hasConceptScore W2145328215C153180895 @default.
- W2145328215 hasConceptScore W2145328215C154945302 @default.
- W2145328215 hasConceptScore W2145328215C185429906 @default.
- W2145328215 hasConceptScore W2145328215C2781067378 @default.
- W2145328215 hasConceptScore W2145328215C33923547 @default.
- W2145328215 hasConceptScore W2145328215C41008148 @default.
- W2145328215 hasConceptScore W2145328215C5274069 @default.
- W2145328215 hasConceptScore W2145328215C71134354 @default.
- W2145328215 hasConceptScore W2145328215C74193536 @default.
- W2145328215 hasConceptScore W2145328215C97931131 @default.
- W2145328215 hasLocation W21453282151 @default.
- W2145328215 hasOpenAccess W2145328215 @default.
- W2145328215 hasPrimaryLocation W21453282151 @default.
- W2145328215 hasRelatedWork W1985231403 @default.
- W2145328215 hasRelatedWork W2037668591 @default.
- W2145328215 hasRelatedWork W2164800850 @default.
- W2145328215 hasRelatedWork W2347937125 @default.
- W2145328215 hasRelatedWork W2553734051 @default.
- W2145328215 hasRelatedWork W2623430991 @default.
- W2145328215 hasRelatedWork W2892261878 @default.
- W2145328215 hasRelatedWork W3123805256 @default.
- W2145328215 hasRelatedWork W3126658412 @default.
- W2145328215 hasRelatedWork W52294986 @default.
- W2145328215 isParatext "false" @default.
- W2145328215 isRetracted "false" @default.
- W2145328215 magId "2145328215" @default.
- W2145328215 workType "article" @default.