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https://academic.oup.com/bioinformatics/article/24/3/412/2748128
Jan 05, 2008 · Results: We propose a hybrid huberized support vector machine (HHSVM). The HHSVM combines the huberized hinge loss function and the elastic-net penalty. By doing so, the HHSVM performs automatic gene selection in a way similar to the L 1-norm SVM. In addition, the HHSVM encourages highly correlated genes to be selected (or removed) together.Cited by: 198
https://www.sciencedirect.com/science/article/pii/S0098135419309330
They are hybrid algorithms that combine the Harris Hawks Optimization algorithm with the Support Vector Machine and k-Nearest neighbors algorithm (k-NN) for the chemical descriptor selection and chemical compound activities. The HHO was firstly introduced in Heidari et al. (2019) and it mimics the cooperative hunting behavior of Harris hawks. Under the HHO scheme, each Harris hawk is a …Author: Essam H. Houssein, Mosa E. Hosney, Diego Oliva, Waleed M. Mohamed, M. Hassaballah
https://www.sciencedirect.com/science/article/pii/S0305048304001082
Support vector machines (SVMs), a novel neural network technique, have been successfully applied in solving nonlinear regression estimation problems. Therefore, this investigation proposes a hybrid methodology that exploits the unique strength of the ARIMA model and the SVMs model in forecasting stock prices problems.Cited by: 693
https://pdfs.semanticscholar.org/2441/8a7d4d971fa8f51974588a113d12ae9644cc.pdf
This resulting hybrid system is categorized as embedded hybrid system where the technologies participating are integrated in such a manner that they appear to be inter-twined. The proposed model is similar to the classical recursive partitioning schemes, except that the leaf nodes created are Support Vector Machine categorizers
https://www.semanticscholar.org/paper/A-hybrid-ARIMA-and-support-vector-machines-model-in-Pai-Lin/2b41f01a548c123ff183197ab0177769505a6872
Support vector machines (SVMs), a novel neural network technique, have been successfully applied in solving nonlinear regression estimation problems. Therefore, this investigation proposes a hybrid methodology that exploits the unique strength of the ARIMA model and the SVMs model in forecasting stock prices problems.
https://www.researchgate.net/publication/221345454_Hybrid_huberized_support_vector_machines_for_microarray_classification
To overcome this limitation, we pro- pose the hybrid huberized support vector ma- chine (HHSVM). The HHSVM uses the hu- berized hinge loss function and the elastic-net penalty. It has two major...
http://www.m-hikari.com/ams/ams-2013/ams-57-60-2013/sujjaviriyasupAMS57-60-2013.pdf
Hybrid ARIMA-support vector machine model 2837 historical data and also use 1-9 historical data to input into the SVM model use kernel function as polynomial with degree and coefficient is 9 and 0.2, respectively, to predict the future numbers of 10 th data.Cited by: 4
https://dl.acm.org/citation.cfm?id=1273620
To overcome this limitation, we propose the hybrid huberized support vector machine (HHSVM). The HHSVM uses the huberized hinge loss function and the elastic-net penalty. It has two major benefits: 1. automatic gene selection; 2. the grouping effect , where highly correlated genes tend to be selected/removed together.Cited by: 62
https://www.researchgate.net/publication/222190824_A_hybrid_ARIMA_and_support_vector_machines_model_in_stock_price_forecasting
Support vector machines (SVMs), a novel neural network technique, have been successfully applied in solving nonlinear regression estimation problems. Therefore, this investigation proposes a hybrid...
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