Matches in SemOpenAlex for { <https://semopenalex.org/work/W150873652> ?p ?o ?g. }
Showing items 1 to 88 of
88
with 100 items per page.
- W150873652 endingPage "94" @default.
- W150873652 startingPage "87" @default.
- W150873652 abstract "We present a hybrid system for generating multiple linear regression lines for the solution of a function approximation problem. The system consists of three components: (1) a neural network which is trained to fit the data samples; (2) a simple algorithm for splitting the input space of the data into subregions and (3) the traditional multiple regression technique for finding the coefficients of the regression lines. While neural networks work particularly well for nonlinear function approximation, their outputs are difficult to explain to a human user. Our proposed system predicts the network outputs as linear functions of the input attributes. In order to maintain the predictive accuracy of the networks, the system divides the space of the input data into several subspaces. In each of these subspaces, a linear equation is generated for predicting the target values for all samples that belong to the subspace. We illustrate the effectiveness of the system using two data sets, one is an artificial data set, while the other is a real world data set for predicting the fuel consumption of automobiles." @default.
- W150873652 created "2016-06-24" @default.
- W150873652 creator A5015925094 @default.
- W150873652 creator A5067016419 @default.
- W150873652 date "2003-01-01" @default.
- W150873652 modified "2023-09-27" @default.
- W150873652 title "A Hybrid Connectionist for Multiple Regression" @default.
- W150873652 cites W1493706908 @default.
- W150873652 cites W1497945199 @default.
- W150873652 cites W1602944711 @default.
- W150873652 cites W1988115241 @default.
- W150873652 cites W2062430377 @default.
- W150873652 cites W2136734237 @default.
- W150873652 doi "https://doi.org/10.1007/978-3-7908-1902-1_12" @default.
- W150873652 hasPublicationYear "2003" @default.
- W150873652 type Work @default.
- W150873652 sameAs 150873652 @default.
- W150873652 citedByCount "0" @default.
- W150873652 crossrefType "book-chapter" @default.
- W150873652 hasAuthorship W150873652A5015925094 @default.
- W150873652 hasAuthorship W150873652A5067016419 @default.
- W150873652 hasConcept C105795698 @default.
- W150873652 hasConcept C11413529 @default.
- W150873652 hasConcept C119857082 @default.
- W150873652 hasConcept C121332964 @default.
- W150873652 hasConcept C12362212 @default.
- W150873652 hasConcept C126255220 @default.
- W150873652 hasConcept C14036430 @default.
- W150873652 hasConcept C154945302 @default.
- W150873652 hasConcept C158622935 @default.
- W150873652 hasConcept C177264268 @default.
- W150873652 hasConcept C199360897 @default.
- W150873652 hasConcept C2524010 @default.
- W150873652 hasConcept C32834561 @default.
- W150873652 hasConcept C33923547 @default.
- W150873652 hasConcept C41008148 @default.
- W150873652 hasConcept C48921125 @default.
- W150873652 hasConcept C50644808 @default.
- W150873652 hasConcept C50897621 @default.
- W150873652 hasConcept C58489278 @default.
- W150873652 hasConcept C62520636 @default.
- W150873652 hasConcept C78458016 @default.
- W150873652 hasConcept C83546350 @default.
- W150873652 hasConcept C86803240 @default.
- W150873652 hasConcept C91873725 @default.
- W150873652 hasConceptScore W150873652C105795698 @default.
- W150873652 hasConceptScore W150873652C11413529 @default.
- W150873652 hasConceptScore W150873652C119857082 @default.
- W150873652 hasConceptScore W150873652C121332964 @default.
- W150873652 hasConceptScore W150873652C12362212 @default.
- W150873652 hasConceptScore W150873652C126255220 @default.
- W150873652 hasConceptScore W150873652C14036430 @default.
- W150873652 hasConceptScore W150873652C154945302 @default.
- W150873652 hasConceptScore W150873652C158622935 @default.
- W150873652 hasConceptScore W150873652C177264268 @default.
- W150873652 hasConceptScore W150873652C199360897 @default.
- W150873652 hasConceptScore W150873652C2524010 @default.
- W150873652 hasConceptScore W150873652C32834561 @default.
- W150873652 hasConceptScore W150873652C33923547 @default.
- W150873652 hasConceptScore W150873652C41008148 @default.
- W150873652 hasConceptScore W150873652C48921125 @default.
- W150873652 hasConceptScore W150873652C50644808 @default.
- W150873652 hasConceptScore W150873652C50897621 @default.
- W150873652 hasConceptScore W150873652C58489278 @default.
- W150873652 hasConceptScore W150873652C62520636 @default.
- W150873652 hasConceptScore W150873652C78458016 @default.
- W150873652 hasConceptScore W150873652C83546350 @default.
- W150873652 hasConceptScore W150873652C86803240 @default.
- W150873652 hasConceptScore W150873652C91873725 @default.
- W150873652 hasLocation W1508736521 @default.
- W150873652 hasOpenAccess W150873652 @default.
- W150873652 hasPrimaryLocation W1508736521 @default.
- W150873652 hasRelatedWork W150873652 @default.
- W150873652 hasRelatedWork W1549146587 @default.
- W150873652 hasRelatedWork W1589781227 @default.
- W150873652 hasRelatedWork W1983392752 @default.
- W150873652 hasRelatedWork W2009138413 @default.
- W150873652 hasRelatedWork W2019425011 @default.
- W150873652 hasRelatedWork W2037021038 @default.
- W150873652 hasRelatedWork W2963941369 @default.
- W150873652 hasRelatedWork W2977185326 @default.
- W150873652 hasRelatedWork W3200356320 @default.
- W150873652 isParatext "false" @default.
- W150873652 isRetracted "false" @default.
- W150873652 magId "150873652" @default.
- W150873652 workType "book-chapter" @default.