A new kernel function of support vector regression combined with probability distribution and its application in chemometrics and the QSAR modeling

2017 ◽  
Vol 167 ◽  
pp. 96-101 ◽  
Author(s):  
Sujie Xue ◽  
Xuefeng Yan
Keyword(s):  
Probability Distribution ◽  
Support Vector Regression ◽  
Kernel Function ◽  
Support Vector ◽  
Qsar Modeling

scholarly journals Initial Optimal Parameters of Artificial Neural Network and Support Vector Regression

2018 ◽  
Vol 8 (5) ◽  
pp. 3341 ◽  
Author(s):  
Edy Fradinata ◽  
Sakesun Suthummanon ◽  
Wannarat Suntiamorntut
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Regression ◽  
Kernel Function ◽  
Activation Function ◽  
Support Vector ◽  
Optimal Parameters ◽  
Artificial Neural ◽  
Hidden Layer ◽  
Linear Loss

This paper presents architecture of backpropagation Artificial Neural Network (ANN) and Support Vector Regression (SVR) models in supervised learning process for cement demand dataset. This study aims to identify the effectiveness of each parameter of mean square error (MSE) indicators for time series dataset. The study varies different random sample in each demand parameter in the network of ANN and support vector function as well. The variations of percent datasets from activation function, learning rate of sigmoid and purelin, hidden layer, neurons, and training function should be applied for ANN. Furthermore, SVR is varied in kernel function, lost function and insensitivity to obtain the best result from its simulation. The best results of this study for ANN activation function is Sigmoid. The amount of data input is 100% or 96 of data, 150 learning rates, one hidden layer, trinlm training function, 15 neurons and 3 total layers. The best results for SVR are six variables that run in optimal condition, kernel function is linear, loss function is ౬-insensitive, and insensitivity was 1. The better results for both methods are six variables. The contribution of this study is to obtain the optimal parameters for specific variables of ANN and SVR.

scholarly journals Parameter Selection Method for Support Vector Regression Based on Adaptive Fusion of the Mixed Kernel Function

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Hailun Wang ◽  
Daxing Xu
Keyword(s):  
Kalman Filter ◽  
Support Vector Regression ◽  
Kernel Function ◽  
Unscented Kalman Filter ◽  
Selection Method ◽  
Parameter Selection ◽  
Support Vector ◽  
Regression Parameters ◽  
Cubature Kalman Filter ◽  
Adaptive Fusion

Support vector regression algorithm is widely used in fault diagnosis of rolling bearing. A new model parameter selection method for support vector regression based on adaptive fusion of the mixed kernel function is proposed in this paper. We choose the mixed kernel function as the kernel function of support vector regression. The mixed kernel function of the fusion coefficients, kernel function parameters, and regression parameters are combined together as the parameters of the state vector. Thus, the model selection problem is transformed into a nonlinear system state estimation problem. We use a 5th-degree cubature Kalman filter to estimate the parameters. In this way, we realize the adaptive selection of mixed kernel function weighted coefficients and the kernel parameters, the regression parameters. Compared with a single kernel function, unscented Kalman filter (UKF) support vector regression algorithms, and genetic algorithms, the decision regression function obtained by the proposed method has better generalization ability and higher prediction accuracy.

scholarly journals Prior-knowledge based Green's kernel for support vector regression

2021 ◽  
Author(s):  
Tahir Farooq
Keyword(s):  
Support Vector Regression ◽  
Prior Knowledge ◽  
Kernel Function ◽  
Real World ◽  
Matched Filter ◽  
Kernel Functions ◽  
Support Vector ◽  
Mathematical Framework ◽  
Knowledge Based ◽  
Model Complex

This thesis presents a novel prior knowledge based Green's kernel for support vector regression (SVR) and provides an empirical investigation of SVM's (support vector machines) ability to model complex real world problems using a real dataset. After reviewing the theoretical background such as theory SVM, the correspondence between kernels functions used in SVM and regularization operators used in regularization networks as well as the use of Green's function of their corresponding regularization operators to construct kernel functions for SVM, a mathematical framework is presented to obtain the domain knowledge about the magnitude of the Fourier transform of the function to be predicted and design a prior knowledge based Green's kernel that exhibits optimal regularization properties by using the concept of matched filters. The matched filter behavior of the proposed kernel function provides the optimal regularization and also makes it suitable for signals corrupted with noise that includes many real world systems. Several experiments, mostly using benchmark datasets ranging from simple regression models to non-linear and high dimensional chaotic time series, have been conducted in order to compare the performance of the proposed technique with the results already published in the literature for other existing support vector kernels over a variety of settings including different noise levels, noise models, loss functions and SVM variations. The proposed kernel function improves the best known results by 18.6% and 24.4% on a benchmark dataset for two different experimental settings.

scholarly journals Probability Distribution and Deviation Information Fusion Driven Support Vector Regression Model and Its Application

2017 ◽  
Vol 2017 ◽  
pp. 1-11
Author(s):  
Changhao Fan ◽  
Xuefeng Yan
Keyword(s):  
Probability Distribution ◽  
Regression Model ◽  
Support Vector Regression ◽  
Predictive Performance ◽  
Training Sample ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Sample Data ◽  
Error Coefficient ◽  
Influence Of Noise

In modeling, only information from the deviation between the output of the support vector regression (SVR) model and the training sample is considered, whereas the other prior information of the training sample, such as probability distribution information, is ignored. Probabilistic distribution information describes the overall distribution of sample data in a training sample that contains different degrees of noise and potential outliers, as well as helping develop a high-accuracy model. To mine and use the probability distribution information of a training sample, a new support vector regression model that incorporates probability distribution information weight SVR (PDISVR) is proposed. In the PDISVR model, the probability distribution of each sample is considered as the weight and is then introduced into the error coefficient and slack variables of SVR. Thus, the deviation and probability distribution information of the training sample are both used in the PDISVR model to eliminate the influence of noise and outliers in the training sample and to improve predictive performance. Furthermore, examples with different degrees of noise were employed to demonstrate the performance of PDISVR, which was then compared with those of three SVR-based methods. The results showed that PDISVR performs better than the three other methods.

scholarly journals Support Vector Regression Method for Wind Speed Prediction Incorporating Probability Prior Knowledge

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Jiqiang Chen ◽  
Xiaoping Xue ◽  
Minghu Ha ◽  
Daren Yu ◽  
Litao Ma
Keyword(s):  
Wind Speed ◽  
Probability Distribution ◽  
Support Vector Regression ◽  
Prior Knowledge ◽  
Historical Data ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Wind Speed Prediction ◽  
Speed Fluctuation ◽  
Speed Prediction

Prior knowledge, such as wind speed probability distribution based on historical data and the wind speed fluctuation between the maximal value and the minimal value in a certain period of time, provides much more information about the wind speed, so it is necessary to incorporate it into the wind speed prediction. First, a method of estimating wind speed probability distribution based on historical data is proposed based on Bernoulli’s law of large numbers. Second, in order to describe the wind speed fluctuation between the maximal value and the minimal value in a certain period of time, the probability distribution estimated by the proposed method is incorporated into the training data and the testing data. Third, a support vector regression model for wind speed prediction is proposed based on standard support vector regression. At last, experiments predicting the wind speed in a certain wind farm show that the proposed method is feasible and effective and the model’s running time and prediction errors can meet the needs of wind speed prediction.

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