Multi RBF-kernel support vector regression for clinical cognitive scores prediction in schizophrenia

2020 ◽  
Author(s):  
Jiayu Wang ◽  
Huixiang Zhuang
Keyword(s):  
Support Vector Regression ◽  
Support Vector ◽  
Rbf Kernel

scholarly journals Research on Amplifier Performance Evaluation Based onδ-Support Vector Regression

2014 ◽  
Vol 2014 ◽  
pp. 1-6
Author(s):  
Xing Huo ◽  
Aihua Zhang ◽  
Hamid Reza Karimi
Keyword(s):  
Performance Evaluation ◽  
Support Vector Regression ◽  
Computer Calculation ◽  
Support Vector ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Evaluation Strategy ◽  
Support Vectors ◽  
Performance Indexes ◽  
Rbf Kernel

Focusing on the amplifier performance evaluation demand, a novel evaluation strategy based onδ-support vector regression (δ-SVR) is proposed in this paper. Lower computer calculation demand is considered firstly. And this is dealt with by the superiority ofδ-SVR which can be significantly improved on the number of support vectors. Moreover, the function ofδ-SVR employs the modified RBF kernel function which is constructed from an original kernel by removing the last coordinate and adding the linear term with the last coordinate. Experiment adopted the typical circuit Sallen-Key low pass filter to prove the proposed evaluation strategy via the eight performance indexes. Simulation results reveal that the need of the number ofδ-SVR support vectors is the lowest among the other two methods LSSVR andε-SVR under obtaining nearly the same evaluation result. And this is also suitable for promotion computational speed.

scholarly journals Comparison of Regression, Support Vector Regression (SVR), and SVR-Particle Swarm Optimization (PSO) for Rainfall Forecasting

2020 ◽  
Vol 5 (3) ◽  
pp. 235
Author(s):  
Fendy Yulianto ◽  
Wayan Firdaus Mahmudy ◽  
Arief Andy Soebroto
Keyword(s):  
Particle Swarm Optimization ◽  
Performance Evaluation ◽  
Support Vector Regression ◽  
Regression Function ◽  
High Accuracy ◽  
Rainfall Data ◽  
Support Vector ◽  
Rainfall Forecasting ◽  
Swarm Optimization ◽  
Rbf Kernel

Rainfall is one of the factors that influence climate change in an area and is very difficult to predict, while rainfall information is very important for the community. Forecasting can be done using existing historical data with the help of mathematical computing in modeling. The Support Vector Regression (SVR) method is one method that can be used to predict non-linear rainfall data using a regression function. In calculations using the regression function, choosing the right SVR parameters is needed to produce forecasting with high accuracy. Particle Swarm Optimization (PSO) method is one method that can be used to optimize the parameters of the existing SVR method, so that it will produce SVR parameter values with high accuracy. Forecasting with rainfall data in Poncokusumo region using SVR-PSO has a performance evaluation value that refers to the value of Root Mean Square Error (RMSE). There are several Kernels that will be used in predicting rainfall using Regression, SVR, and SVR-PSO with Linear Kernels, Gaussian RBF Kernels, ANOVA RBF Kernels. The results of the performance evaluation values obtained by referring to the RMSE value for Regression is 56,098, SVR is 88,426, SVR-PSO method with Linear Kernel is 7.998, SVR-PSO method with Gaussian RBF Kernel is 27.172, and SVR-PSO method with ANOVA RBF Kernel is 2.193. Based on research that has been done, ANOVA RBF Kernel is a good Kernel on the SVR-PSO method for use in rainfall forecasting, because it has the best forecasting accuracy with the smallest RMSE value.

scholarly journals Flood and Drought Prediction Using the Machine Learning Algorithm Support Vector Regression

2019 ◽  
Vol 9 (1) ◽  
pp. 5194-5199
Keyword(s):  
Machine Learning ◽  
Support Vector Regression ◽  
Predictive Model ◽  
Learning Algorithm ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Predictive Skill ◽  
Drought Prediction ◽  
Rbf Kernel ◽  
Search Approach

Flood and drought are frequently happening natural disasters in most of the countries. These disasters can cause considerable damage to agriculture, ecology and economy of the country. Mitigating the impacts of flood and drought is a valuable help to the human being. The main cause of these disasters is precipitation. If the past precipitation data are analyzed properly, the future flood and drought events can be easily found. Prediction using the Standard Precipitation Index (SPI) is a way to find the wet or dry condition of a region or country. In this paper the SPI values with different lead times are calculated for a long period of time. These SPI indices are analysed by a predictive model using the machine learning algorithm called Support Vector Regression (SVR) with RBF (Radial Basis Function) kernel. In this model the Grid Search approach is used for optimization. The forecast result of this predictive model shows the predictive skill of the SVR-RBF kernel.

scholarly journals Support Vector Regression for Modeling Effect of Education Rate on Life Expectancy Rate in Indonesia

2018 ◽  
pp. 64
Author(s):  
Muhammad Ghazali ◽  
Ita Fitriati ◽  
Ramdani Purnamasari
Keyword(s):  
Life Expectancy ◽  
Support Vector Regression ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Mathematics Model ◽  
Research Support ◽  
The People ◽  
Development Policies ◽  
Rbf Kernel ◽  
The Government

Life Expectancy Rate is the average number of years of life that is lived by someone who has reached a certain age. Life Expectancy is a tool to evaluate the government performance in improving the prosperity of the people. Studies on the factors that influence Life Expectancy Rate are needed to reach more accurate mathematics model to provide a better consideration for the government to determine the direction of future development policies. The data used in this study were derived from SUSENAS data with the objects of observations are all districts/cities in Indonesia in 2012. In this research, Support Vector Regression (SVR) method is used to estimate the effect of education factor which is represented by length of education by years (X) on Life Expectancy Rate (Y). Support Vector Regression (SVR) model in this research used several different kernels such as  polynomial kernel, RBF and Exponential RBF (ERBF) to find the best model. The best model criterion is the model that produces the largest R2 value. The best model resulted in this research is a model that uses Exponential RBF kernel.

scholarly journals Planning Total Veener Production PT. XYZ

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Krisna Risky Putra Irawan ◽  
Tedjo Sukmono
Keyword(s):  
Support Vector Regression ◽  
Time Series Data ◽  
Training Data ◽  
Series Data ◽  
Support Vector ◽  
Efficient Production ◽  
Production Forecasting ◽  
Data Application ◽  
Rbf Kernel ◽  
Accurate Forecast

PT. XYZ is engaged in the manufacture and sale of wood veneers. Starting from the constant occurrence of over stock, now the company must make improvements to the production forecasting process so that over stock can be avoided. It can be seen that accurate production forecasting can create conditions for an effective and efficient production system. This study aims to obtain a more accurate forecast of material requirements using the Support Vector Regression (SVR) method, which is the result of the development of a Support Vector Machine (SVM) which has good performance in predicting time series data. Application of the Support Vector Regression (SVR) method with the RBF kernel in predicting the need for veneer production using the MATLAB application, it produces the smallest error rate with a MAPE of 5%, RMSE of 4364.63 and of 0.748274147. on  67 training data and 20 testing data.

Local and Regional Hour-Ahead Forecasts of Solar Irradiance with Training Data Selection and Support Vector Regression

2016 ◽  
Vol 136 (12) ◽  
pp. 898-907 ◽  
Author(s):  
Joao Gari da Silva Fonseca Junior ◽  
Hideaki Ohtake ◽  
Takashi Oozeki ◽  
Kazuhiko Ogimoto
Keyword(s):  
Support Vector Regression ◽  
Solar Irradiance ◽  
Training Data ◽  
Data Selection ◽  
Support Vector ◽  
Training Data Selection

The Spinning Quality Control Management Based on Decision Making by Data Mining Techniques

2018 ◽  
Vol 7 (1) ◽  
pp. 72
Author(s):  
Khalid AA Abakar ◽  
Chongwen Yu
Keyword(s):  
Data Mining ◽  
Kernel Functions ◽  
Support Vector ◽  
Ann Model ◽  
Data Mining Techniques ◽  
Yarn Quality ◽  
Yarn Properties ◽  
Svm Model ◽  
Rbf Kernel

This work demonstrated the possibility of using the data mining techniques such as artificial neural networks (ANN) and support vector machine (SVM) based model to predict the quality of the spinning yarn parameters. Three different kernel functions were used as SVM kernel functions which are Polynomial and Radial Basis Function (RBF) and Pearson VII Function-based Universal Kernel (PUK) and ANN model were used as data mining techniques to predict yarn properties. In this paper, it was found that the SVM model based on Person VII kernel function (PUK) have the same performance in prediction of spinning yarn quality in comparison with SVM based RBF kernel. The comparison with the ANN model showed that the two SVM models give a better prediction performance than an ANN model.

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