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https://journals.ametsoc.org/doi/full/10.1175/WAF-D-11-00004.1
Apr 01, 2012 · Wavelet Support Vector Machines for Forecasting Precipitation in Tropical Cyclones: Comparisons with GSVM, Regression, and MM5Cited by: 24
https://www.researchgate.net/publication/8345770_Wavelet_Support_Vector_Machine
Rank wavelet support vector machine (rank-WSVM) [27, 28] is proposed to apply in the classification of complex disturbances. A new method for the classification of PQ disturbances was proposed by ...
https://journals.ametsoc.org/doi/10.1175/JHM-D-14-0210.1
Oct 01, 2015 · In the first step, the discrete wavelet transform is coupled with a support vector regression model for streamflow prediction. The three key factors of mother wavelets, decomposition levels, and edge effects are considered in the wavelet decomposition phase when using the hybrid wavelet–support vector regression model (WS model).Cited by: 13
https://www.sciencedirect.com/science/article/pii/S0022169414005101
This study investigated the performance and potential of a hybrid model that combined the discrete wavelet transform and support vector regression (the DWT–SVR model) …Cited by: 52
https://www.sciencedirect.com/science/article/pii/S0022169413007968
Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet neural network and wavelet support vector regression models. Author links open overlay panel A ... SVMs can be divided into two main techniques, the Support Vector Classification (SVC) and Support Vector Regression (SVR), which address problems of ...Cited by: 183
https://www.hindawi.com/journals/acisc/2012/794061/
The Standard Precipitation Index (SPI) is forecast and compared using artificial neural networks (ANNs), support vector regression (SVR), and wavelet neural networks (WN). SPI 3 and SPI 12 were the SPI values that were forecasted. ... Applied Computational Intelligence and Soft Computing is a peer-reviewed, Open Access journal that focuses on ...Cited by: 85
http://gravis.dmi.unibas.ch/publications/2012/CRV2012_Raetsch.pdf
Wavelet Reduced Support Vector Regression for Efficient and Robust Head Pose Estimation Matthias Ratsch¨ (Member IEEE), Philip Quick (Member IEEE) Cognitec Systems GmbH Grossenhainer Str. 101, D-01127 Dresden, Germany
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http://www.umich.edu/~racelab/static/Webpublication/2009-MATCOM-CharlesL.pdf
However, for the support vector regression with the standard quadratic programming technique, the implementation is computationally expensive and sufficient model sparsity cannot be guaranteed. In this article, from the perspective of model sparsity, the linear programming support vector regression (LP-SVR) with wavelet kernel was proposed,
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