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1.Pairing support vector algorithm for data regression

作者：Pei-Yi Hao ; ^{haupy@cc.kuas.edu.tw}Author Vitae

关键词：Support vector regression ; Twin support vector regression ; Noise heteroscedastic model ; Parametric insensitive model ; Nonparallel functions ; Sparse regression

刊名：Neurocomputing

出版年：2017

2.Multiple sources and multiple measures based traffic flow prediction using the chaos theory and support vector regression method

作者：Anyu Cheng^a ; Xiao Jiang^a ; Yongfu Li^a ; ^{laf1212@163.com" class="auth_mail" title="E-mail the corresponding author} ; Chao Zhang^b ; Hao Zhu^a

关键词：Traffic flow ; Chaos theory ; Bayesian estimation theory ; Support vector regression ; Phase space reconstruction ; Multiple measures

刊名：Physica A: Statistical Mechanics and its Applications

出版年：2017

3.Short-term power load probability density forecasting method using kernel-based support vector quantile regression and Copula theory

作者：Yaoyao He^a ; ^b ; ^c ; ^{hy-342501y@163.com} ; Rui Liu^a ; ^b ; ^{lr_0329@163.com} ; Haiyan Li^a ; ^b ; Shuo Wang^c ; Xiaofen Lu^c

关键词：Short-term power load probability density forecasting ; Support vector quantile regression ; PI coverage probability ; PI normalized average width ; Copula theory ; Real-time price

刊名：Applied Energy

出版年：2017

4.European Exchange Trading Funds Trading with Locally Weighted Support Vector Regression

作者：Georgios Sermpinis^a ; ^{georgios.sermpinis@glasgow.ac.uk} ; Charalampos Stasinakis^a ; ^{charalampos.stasinakis@glasgow.ac.uk} ; Rafael Rosillo^b ; ^{rrosc@unileon.es} ; David de la Fuente^c ; ^{david@uniovi.es}

关键词：Locally Weighted Support Vector Regression ; Support Vector Regression ; Kernels ; Trading ; Exchange Traded Funds

刊名：European Journal of Operational Research

出版年：2017

5.Simulation of groundwater level variations using wavelet combined with neural network, linear regression and support vector machine

作者：Hadi Ebrahimi ; ^{hadi_e1360@yahoo.com} ; Taher Rajaee ^{taher_rajaee@yahoo.com} ^{trajaee@qom.ac.ir}

关键词：Time delay neural networks ; Wavelet ; Iran ; Multi linear regression ; Groundwater level ; Support vector machine

刊名：Global and Planetary Change

出版年：2017

6.Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model

作者：Ravinesh C Deo^a ; ^{ravinesh.deo@usq.edu.au} ; Ozgur Kisi^b ; Vijay P Singh^c ; ^d

关键词：Standardized precipitation index ; Drought forecasting ; Multivariate adaptive regression spline ; Least square support vector machine ; M5Tree model

刊名：Atmospheric Research

出版年：2017

7.The QSPR models to predict the solubility of CO₂ in ionic liquids based on least-squares support vector machines and genetic algorithm-multi linear regression

作者：Iliad Mehraein ; Siavash Riahi ; ^{riahi@ut.ac.ir}

关键词：Ionic liquids ; Solubility ; Carbon dioxide ; QSPR ; Multi-linear regression ; Support vector machines

刊名：Journal of Molecular Liquids

出版年：2017

8.A hybrid particle swarm optimization and support vector regression model for modelling permeability prediction of hydrocarbon reservoir

作者：Kabiru. O. Akande^a ; ^{koakande@gmail.com} ; Taoreed. O. Owolabi^b ; ^e ; ^{owolabitaoreedolakunle@gmail.com} ; Sunday. O. Olatunji^c ; ^{oluolatunji.aadam@gmail.com} ; AbdulAzeez AbdulRaheem^d ; ^{aazeez@kfupm.edu.sa}

关键词：Particle swarm optimization (PSO) ; Support vector regression (SVR) ; Hydrocarbon reservoir ; Permeability prediction ; Optimization techniques

刊名：Journal of Petroleum Science and Engineering

出版年：2017

9.Unravelling effects of flavanols and their derivatives on acrylamide formation via support vector machine modelling

关键词：Acrylamide ; Low moisture ; Flavanols and derivatives ; Dual effect ; Maillard reaction ; Support vector regression

刊名：Food Chemistry

出版年：2017

10.Empirical approach for strength prediction of geopolymer stabilized clayey soil using support vector machines

作者：Ruhul Amin Mozumder ; Aminul Islam Laskar ; ^{aminul.nits@gmail.com} ; Monowar Hussain

关键词：Geopolymer ; Ground-granulated blast furnace slag ; Soil stabilization ; Support vector regression

刊名：Construction and Building Materials

出版年：2017

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