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nonparametric approach based on a combination of kernel logistic regression and ¡support vector regression. …
Persistent link: https://www.econbiz.de/10010516923
The paper brings together methods from two disciplines: machine learning theory and robust statistics. Robustness …. Kernel logistic regression, support vector machines, least squares and the AdaBoost loss function are treated as special …
Persistent link: https://www.econbiz.de/10010477496
We consider theoretical bootstrap "coupling" techniques for nonparametric robust smoothers and quantile regression, and verify the bootstrap improvement. To cope with curse of dimensionality, a variant of "coupling" bootstrap techniques are developed for additive models with both symmetric error...
Persistent link: https://www.econbiz.de/10010195959
This article proposes a simple and fast approach to build simultaneous confi dence bands and perform specification tests for smooth curves in additive models. The method allows for handling of spatially heterogeneous functions and its derivatives as well as heteroscedasticity in the data. It is...
Persistent link: https://www.econbiz.de/10010342897
-gram frequency vectors and train a support vector machine (SVM), relying only on titles, abstracts and IPC categorization of each … document. Altering the utilized Kernel functions and respective parameters we reach a recall level of 83% and precision level …
Persistent link: https://www.econbiz.de/10011342183
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Persistent link: https://www.econbiz.de/10013041345
elections; 2016 and 2020 US. Three models, Support Vector Machine (SVM), Neural Network (NN) and ARIMA models are then used to … individual models, SVM, NN, and ARIMA. In addition, this compared to the single prediction market IOWA Electronic Markets. The …
Persistent link: https://www.econbiz.de/10013177191
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