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在“
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”中,
命中:
1,671
条,耗时:0.0779539 秒
在所有数据库中总计命中:
3,241
条
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 CO
2
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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