Matches in SemOpenAlex for { <https://semopenalex.org/work/W4282016089> ?p ?o ?g. }
Showing items 1 to 89 of
89
with 100 items per page.
- W4282016089 endingPage "109067" @default.
- W4282016089 startingPage "109067" @default.
- W4282016089 abstract "The boosting model is a kind of ensemble learning technology, including XGBoost and GBDT, which take decision trees as weak classifiers and achieve better results in classification and regression problems. The neural network has an excellent performance on image and voice recognition, but its weak interpretability limits on developing a fusion model. By referring to principles and methods of traditional boosting models, we proposed a Neural Network Boosting (NNBoost) regression, which takes shallow neural networks with simple structures as weak classifiers. The NNBoost is a new ensemble learning method, which obtains low regression errors on several data sets. The target loss function of NNBoost is approximated by the Taylor expansion. By inducing the derivative form of NNBoost, we give a gradient descent algorithm. The structure of deep learning is complex, and there are some problems such as gradient disappearing, weak interpretability, and parameters difficult to be adjusted. We use the integration of simple neural networks to alleviate the gradient vanishing problem which is laborious to be solved in deep learning, and conquer the overfitting of a learning algorithm. Finally, through testing on some experiments, the correctness and effectiveness of NNBoost are verified from multiple angles, the effect of multiple shallow neural network fusion is proved, and the development path of boosting idea and deep learning is widened to a certain extent." @default.
- W4282016089 created "2022-06-13" @default.
- W4282016089 creator A5002318539 @default.
- W4282016089 creator A5014260444 @default.
- W4282016089 creator A5020748655 @default.
- W4282016089 creator A5049980642 @default.
- W4282016089 creator A5062153725 @default.
- W4282016089 date "2022-08-01" @default.
- W4282016089 modified "2023-10-06" @default.
- W4282016089 title "A neural network boosting regression model based on XGBoost" @default.
- W4282016089 cites W1498436455 @default.
- W4282016089 cites W1678356000 @default.
- W4282016089 cites W1988790447 @default.
- W4282016089 cites W2064769840 @default.
- W4282016089 cites W2112796928 @default.
- W4282016089 cites W2136922672 @default.
- W4282016089 cites W2142399242 @default.
- W4282016089 cites W2147800946 @default.
- W4282016089 cites W2257979135 @default.
- W4282016089 cites W3124028608 @default.
- W4282016089 cites W3159897038 @default.
- W4282016089 cites W3171080874 @default.
- W4282016089 cites W3179082817 @default.
- W4282016089 cites W3199862920 @default.
- W4282016089 doi "https://doi.org/10.1016/j.asoc.2022.109067" @default.
- W4282016089 hasPublicationYear "2022" @default.
- W4282016089 type Work @default.
- W4282016089 citedByCount "9" @default.
- W4282016089 countsByYear W42820160892023 @default.
- W4282016089 crossrefType "journal-article" @default.
- W4282016089 hasAuthorship W4282016089A5002318539 @default.
- W4282016089 hasAuthorship W4282016089A5014260444 @default.
- W4282016089 hasAuthorship W4282016089A5020748655 @default.
- W4282016089 hasAuthorship W4282016089A5049980642 @default.
- W4282016089 hasAuthorship W4282016089A5062153725 @default.
- W4282016089 hasConcept C105795698 @default.
- W4282016089 hasConcept C108583219 @default.
- W4282016089 hasConcept C119857082 @default.
- W4282016089 hasConcept C153180895 @default.
- W4282016089 hasConcept C153258448 @default.
- W4282016089 hasConcept C154945302 @default.
- W4282016089 hasConcept C169258074 @default.
- W4282016089 hasConcept C22019652 @default.
- W4282016089 hasConcept C2781067378 @default.
- W4282016089 hasConcept C33923547 @default.
- W4282016089 hasConcept C41008148 @default.
- W4282016089 hasConcept C45942800 @default.
- W4282016089 hasConcept C46686674 @default.
- W4282016089 hasConcept C50644808 @default.
- W4282016089 hasConcept C70153297 @default.
- W4282016089 hasConcept C81363708 @default.
- W4282016089 hasConcept C83546350 @default.
- W4282016089 hasConceptScore W4282016089C105795698 @default.
- W4282016089 hasConceptScore W4282016089C108583219 @default.
- W4282016089 hasConceptScore W4282016089C119857082 @default.
- W4282016089 hasConceptScore W4282016089C153180895 @default.
- W4282016089 hasConceptScore W4282016089C153258448 @default.
- W4282016089 hasConceptScore W4282016089C154945302 @default.
- W4282016089 hasConceptScore W4282016089C169258074 @default.
- W4282016089 hasConceptScore W4282016089C22019652 @default.
- W4282016089 hasConceptScore W4282016089C2781067378 @default.
- W4282016089 hasConceptScore W4282016089C33923547 @default.
- W4282016089 hasConceptScore W4282016089C41008148 @default.
- W4282016089 hasConceptScore W4282016089C45942800 @default.
- W4282016089 hasConceptScore W4282016089C46686674 @default.
- W4282016089 hasConceptScore W4282016089C50644808 @default.
- W4282016089 hasConceptScore W4282016089C70153297 @default.
- W4282016089 hasConceptScore W4282016089C81363708 @default.
- W4282016089 hasConceptScore W4282016089C83546350 @default.
- W4282016089 hasFunder F4320321001 @default.
- W4282016089 hasLocation W42820160891 @default.
- W4282016089 hasOpenAccess W4282016089 @default.
- W4282016089 hasPrimaryLocation W42820160891 @default.
- W4282016089 hasRelatedWork W2595706594 @default.
- W4282016089 hasRelatedWork W2890560140 @default.
- W4282016089 hasRelatedWork W3099765033 @default.
- W4282016089 hasRelatedWork W3101544650 @default.
- W4282016089 hasRelatedWork W3124507766 @default.
- W4282016089 hasRelatedWork W4282016089 @default.
- W4282016089 hasRelatedWork W4286908443 @default.
- W4282016089 hasRelatedWork W4313488044 @default.
- W4282016089 hasRelatedWork W4320854072 @default.
- W4282016089 hasRelatedWork W4381383350 @default.
- W4282016089 hasVolume "125" @default.
- W4282016089 isParatext "false" @default.
- W4282016089 isRetracted "false" @default.
- W4282016089 workType "article" @default.