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 A COMPARISON STUDY OF DIFFERENT KERNEL FUNCTIONS FOR SVM-BASED CLASSIFICATION OF MULTI-TEMPORAL POLARIMETRY SAR DATA

2014 ◽  
Vol XL-2/W3 ◽  
pp. 281-285 ◽  
Author(s):  
B. Yekkehkhany ◽  
A. Safari ◽  
S. Homayouni ◽  
M. Hasanlou
Keyword(s):  
Kernel Function ◽  
Temporal Integration ◽  
Experimental Tests ◽  
Kernel Functions ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Svm Classifier ◽  
Rbf Kernel ◽  
Multi Temporal ◽  
Dimension Space

In this paper, a framework is developed based on Support Vector Machines (SVM) for crop classification using polarimetric features extracted from multi-temporal Synthetic Aperture Radar (SAR) imageries. The multi-temporal integration of data not only improves the overall retrieval accuracy but also provides more reliable estimates with respect to single-date data. Several kernel functions are employed and compared in this study for mapping the input space to higher Hilbert dimension space. These kernel functions include linear, polynomials and Radial Based Function (RBF). <br><br> The method is applied to several UAVSAR L-band SAR images acquired over an agricultural area near Winnipeg, Manitoba, Canada. In this research, the temporal alpha features of H/A/α decomposition method are used in classification. The experimental tests show an SVM classifier with RBF kernel for three dates of data increases the Overall Accuracy (OA) to up to 3% in comparison to using linear kernel function, and up to 1% in comparison to a 3rd degree polynomial kernel function.

scholarly journals PREDICTION OF OZONE (O3) VALUES USING SUPPORT VECTOR REGRESSION METHOD

2021 ◽  
Vol 7 (4) ◽  
pp. 81-88
Author(s):  
Chasandra Puspitasari ◽  
Nur Rokhman ◽  
Wahyono
Keyword(s):  
Air Quality ◽  
Support Vector Regression ◽  
Time Series Data ◽  
Motor Vehicles ◽  
Primary Data ◽  
Series Data ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Linear Kernel ◽  
Air Quality Prediction

A large number of motor vehicles that cause congestion is a major factor in the poor air quality in big cities. Ozone (O3) is one of the main indicators in measuring the level of air pollution in the city of Surabaya to find out how air quality. Prediction of Ozone (O3) value is important as a support for the community and government in efforts to improve the air quality. This study aims to predict the value of Ozone (O3) in the form of time series data using the Support Vector Regression (SVR) method with the Linear, Polynomial, RBF, and ANOVA kernels. The data used in this study are 549 primary data from the daily average of ozone (O3) value of Surabaya in the period 1 July 2017 - 31 December 2018. The data will be used in the training and testing process until prediction results are obtained. The results obtained from this study are the Linear kernel produces the best prediction model with a MAPE value of 21.78% with a parameter value 𝜆 = 0.3; 𝜀 = 0.00001; cLR = 0.005; and C = 0.5. The results of the Polynomial kernel are not much different from the Linear kernel which has a MAPE value of 21.83%. While the RBF and ANOVA kernels each produce a model with MAPE value of 24.49% and 22.0%. These results indicate that the SVR method with the kernels used can predict Ozone values quite well.

Using Support Vector Regression in the Study of Product Form Images

2008 ◽  
Author(s):  
Meng-Dar Shieh ◽  
Hsin-En Fang
Keyword(s):  
Support Vector Regression ◽  
Optimal Parameter ◽  
Polynomial Kernel ◽  
Local Characteristic ◽  
Support Vector ◽  
Distributed Data ◽  
Training Models ◽  
Target Values ◽  
Product Image ◽  
Value Sets

In this paper, Support Vector Regression (SVR) training models using three different kernels: polynomial, Radial Basis Function (RBF), and mixed kernels, are constructed to demonstrate the training performance of unarranged data obtained from 32 virtual 3-D computer models. The 32 samples used as input data for training the three SVR models are represented by the coordination value sets of points extracted from 3-D models built by the 3-D software according to the shapes of 32 actual hairdryer products. To train the SVR model, an adjective (streamline) is used to evaluate all the 32 samples by 37 subjects. Then the scores of all the subjects are averaged to be the target values of the training models. In addition, a technique called k-fold cross-validation (C-V) is used to find the optimal parameter combination for optimizing the SVR models. The performance of the SVR using these three kernels to estimate the product image values is determined by the values of the Root Mean Square Error (RMSE). The results show that the optimal SVR model using the polynomial kernel performed better than the one using the RBF kernel. However, it is important to note that the mixed kernel had the best performance of the three. It is also shown in this study that the single RBF has a local characteristic and cannot process the broadly distributed data well. It can, however, be used to improve the power of the SVR by combining with the polynomial kernel.

scholarly journals Pancreatic Cancer Early Detection Using Twin Support Vector Machine Based on Kernel

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 667
Author(s):  
Wismaji Sadewo ◽  
Zuherman Rustam ◽  
Hamidah Hamidah ◽  
Alifah Roudhoh Chusmarsyah
Keyword(s):  
Machine Learning ◽  
Pancreatic Cancer ◽  
Support Vector Machine ◽  
Early Detection ◽  
Feature Space ◽  
Kernel Functions ◽  
Twin Support Vector Machine ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Rbf Kernel

Early detection of pancreatic cancer is difficult, and thus many cases of pancreatic cancer are diagnosed late. When pancreatic cancer is detected, the cancer is usually well developed. Machine learning is an approach that is part of artificial intelligence and can detect pancreatic cancer early. This paper proposes a machine learning approach with the twin support vector machine (TWSVM) method as a new approach to detecting pancreatic cancer early. TWSVM aims to find two symmetry planes such that each plane has a distance close to one data class and as far as possible from another data class. TWSVM is fast in building a model and has good generalizations. However, TWSVM requires kernel functions to operate in the feature space. The kernel functions commonly used are the linear kernel, polynomial kernel, and radial basis function (RBF) kernel. This paper uses the TWSVM method with these kernels and compares the best kernel for use by TWSVM to detect pancreatic cancer early. In this paper, the TWSVM model with each kernel is evaluated using a 10-fold cross validation. The results obtained are that TWSVM based on the kernel is able to detect pancreatic cancer with good performance. However, the best kernel obtained is the RBF kernel, which produces an accuracy of 98%, a sensitivity of 97%, a specificity of 100%, and a running time of around 1.3408 s.

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 some of kernel functions with support vector machines classifier for thalassemia dataset

2021 ◽  
Vol 10 (2) ◽  
pp. 430
Author(s):  
Ilsya Wirasati ◽  
Zuherman Rustam ◽  
Jane Eva Aurelia ◽  
Sri Hartini ◽  
Glori Stephani Saragih
Keyword(s):  
Kernel Functions ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Medical Field ◽  
Treatment Technology ◽  
Blood Disorder ◽  
Vector Machines ◽  
Improve Accuracy ◽  
Rbf Kernel

<span id="docs-internal-guid-9a30056f-7fff-8ff1-59e1-69f89f4280bd"><span>In the medical field, accurate classification of medical data is really important because of its impact on disease detection and patient’s treatment. Technology, machine learning, is needed to help medical staff to improve accuracy to classify disease. This research discussed some kernel functions, such as gaussian radial basis function (RBF) kernel, Polynomial kernel, and linear kernel with support vector machine (SVM) to classify thalassemia data. Thalassemia is a genetic blood disorder which is also one of the major public health problems. In this paper, there is an explanation about thalassemia, SVM, and some of the kernel functions that serve as a comprehensive source for the next research about this topic. Furthermore, there is a comparison result from three kernel functions to find out which one has the best performance. The result is gaussian RBF kernel with SVM is the best method with an average of accuracy 99,63%. </span></span>

scholarly journals Feature Extraction and Classification of MRI Using Hybrid RBF Kernel and SVM

2021 ◽  
pp. 418-426
Author(s):  
Suhas S ◽  
Dr. C. R. Venugopal
Keyword(s):  
Support Vector Machine ◽  
Image Retrieval ◽  
Classification Accuracy ◽  
Kernel Functions ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Svm Classifier ◽  
Mr Images ◽  
Rbf Kernel

An enhanced classification system for classification of MR images using association of kernels with support vector machine is developed and presented in this paper along with the design and development of content-based image retrieval (CBIR) system. Content of image retrieval is the process of finding relevant image from large collection of image database using visual queries. Medical images have led to growth in large image collection. Oriented Rician Noise Reduction Anisotropic Diffusion filter is used for image denoising. A modified hybrid Otsu algorithm termed is used for image segmentation. The texture features are extracted using GLCM method. Genetic algorithm with Joint entropy is adopted for feature selection. The classification is done by support vector machine along with various kernels and the performance is validated. A classification accuracy of 98.83% is obtained using SVM with GRBF kernel. Various features have been extracted and these features are used to classify MR images into five different categories. Performance of the MC-SVM classifier is compared with different kernel functions. From the analysis and performance measures like classification accuracy, it is inferred that the brain and spinal cord MRI classification is best done using MC- SVM with Gaussian RBF kernel function than linear and polynomial kernel functions. The proposed system can provide best classification performance with high accuracy and low error rate.

Improving Sparsity in Kernelized Nonlinear Feature Extraction Algorithms by Polynomial Kernel Higher Order Neural Networks

2010 ◽  
pp. 223-238 ◽  
Author(s):  
Zhao Lu ◽  
Gangbing Song ◽  
Leang-san Shieh
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Support Vector Regression ◽  
Higher Order ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Higher Order Neural Networks ◽  
Programming Support ◽  
Nonlinear Feature Extraction ◽  
Nonlinear Feature

As a general framework to represent data, the kernel method can be used if the interactions between elements of the domain occur only through inner product. As a major stride towards the nonlinear feature extraction and dimension reduction, two important kernel-based feature extraction algorithms, kernel principal component analysis and kernel Fisher discriminant, have been proposed. They are both used to create a projection of multivariate data onto a space of lower dimensionality, while attempting to preserve as much of the structural nature of the data as possible. However, both methods suffer from the complete loss of sparsity and redundancy in the nonlinear feature representation. In an attempt to mitigate these drawbacks, this article focuses on the application of the newly developed polynomial kernel higher order neural networks in improving the sparsity and thereby obtaining a succinct representation for kernel-based nonlinear feature extraction algorithms. Particularly, the learning algorithm is based on linear programming support vector regression, which outperforms the conventional quadratic programming support vector regression in model sparsity and computational efficiency.

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