scholarly journals Using a least squares support vector machine to estimate a local geometric geoid model

2014 ◽  
Vol 20 (2) ◽  
pp. 427-443 ◽  
Author(s):  
Szu-Pyng Kao ◽  
Chao-Nan Chen ◽  
Hui-Chi Huang ◽  
Yu-Ting Shen
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Curve Surface ◽  
Optimal Solution ◽  
Kernel Functions ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Geoid Model ◽  
Geoidal Undulation ◽  
Orthometric Heights

In this study, test-region global positioning system (GPS) control points exhibiting known first-order orthometric heights were employed to obtain the points of plane coordinates and ellipsoidal heights by using the real-time GPS kinematic measurement method. Plane-fitting, second-order curve-surface fitting, back-propagation (BP) neural networks, and least-squares support vector machine (LS-SVM) calculation methods were employed. The study includes a discussion on data integrity and localization, changing reference-point quantities and distributions to obtain an optimal solution. Furthermore, the LS-SVM was combined with local geoidal-undulation models that were established by researching and analyzing3 kernel functions. The results indicated that the overall precision of the local geometric geoidal-undulation values calculated using the radial basis function (RBF) and third-order polynomial kernel function was optimal and the root mean square error (RMSE) was approximately ± 1.5 cm. These findings demonstrated that the LS-SVM provides a rapid and practical method for determining orthometric heights and should serve as a valuable academic reference regarding local geoid models.

The Contrastive Analysis of Predicting Riveting Pieces of Corrosion Fatigue Based on Multiple Kernel Function

2014 ◽  
Vol 687-691 ◽  
pp. 587-592
Author(s):  
Jian Fei Chen ◽  
Gang Jiang ◽  
Zi Sheng Li ◽  
Jian Feng Yang
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Corrosion Fatigue ◽  
Corrosion Damage ◽  
Kernel Functions ◽  
Support Vector ◽  
Grey System ◽  
Damage Prediction ◽  
Mechanical Structure ◽  
Long Term Storage

In the process of long-term storage, the equipment would happen storage environment contaminated corrosion, mechanical structure stress corrosion damage. Currently,the corrosion fatigue damage prediction accuracy of method was low. Different kernel functions were adopted by this paper to compare based on least squares support vector machine (LSSVM). Besides, comparison was made among the BP neural network method, Standard Support Vector Machines (SVM), Grey System Prediction model Method and the radial basis function kernel least squares support vector machine (RBF_LSSVM) method by the simulation experiment. The optimal results finally were applied to practical engineering. The results showed that high accuracy and performance could be gained by employing the RBF_LSSVM method for predicting the trends of the mechanical structure rivet corrosion.

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 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.

scholarly journals New Mixed Kernel Functions of SVM Used in Pattern Recognition

2016 ◽  
Vol 16 (5) ◽  
pp. 5-14 ◽  
Author(s):  
Hao Huanrui
Keyword(s):  
Support Vector Machine ◽  
Kernel Function ◽  
Kernel Methods ◽  
Pattern Analysis ◽  
New Technology ◽  
Wavelet Function ◽  
Kernel Functions ◽  
Good Time ◽  
Polynomial Kernel ◽  
Support Vector

Abstract The pattern analysis technology based on kernel methods is a new technology, which combines good performance and strict theory. With support vector machine, pattern analysis is easy and fast. But the existing kernel function fits the requirement. In the paper, we explore the new mixed kernel functions which are mixed with Gaussian and Wavelet function, Gaussian and Polynomial kernel function. With the new mixed kernel functions, we check different parameters. The results shows that the new mixed kernel functions have good time efficiency and accuracy. In image recognition we used SVM with two mixed kernel functions, the mixed kernel function of Gaussian and Wavelet function are suitable for more states.

scholarly journals Housing Value Forecasting Based on Machine Learning Methods

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Jingyi Mu ◽  
Fang Wu ◽  
Aihua Zhang
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Big Data ◽  
Least Squares ◽  
Optimal Solution ◽  
Support Vector ◽  
Learning Methods ◽  
Data Set ◽  
Machine Learning Methods ◽  
The Government

In the era of big data, many urgent issues to tackle in all walks of life all can be solved via big data technique. Compared with the Internet, economy, industry, and aerospace fields, the application of big data in the area of architecture is relatively few. In this paper, on the basis of the actual data, the values of Boston suburb houses are forecast by several machine learning methods. According to the predictions, the government and developers can make decisions about whether developing the real estate on corresponding regions or not. In this paper, support vector machine (SVM), least squares support vector machine (LSSVM), and partial least squares (PLS) methods are used to forecast the home values. And these algorithms are compared according to the predicted results. Experiment shows that although the data set exists serious nonlinearity, the experiment result also show SVM and LSSVM methods are superior to PLS on dealing with the problem of nonlinearity. The global optimal solution can be found and best forecasting effect can be achieved by SVM because of solving a quadratic programming problem. In this paper, the different computation efficiencies of the algorithms are compared according to the computing times of relevant algorithms.

Forecasting performance of support vector machine for the Poyang Lake's water level

2014 ◽  
Vol 70 (9) ◽  
pp. 1488-1495 ◽  
Author(s):  
Yingying Lan
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Model Performance ◽  
Optimal Solution ◽  
Kernel Functions ◽  
Support Vector ◽  
Hydrologic Time Series ◽  
Forecasting Performance ◽  
Linear Svm

The growth of forecasting models has resulted in the development of an excellent model known as the support vector machine (SVM). SVMs can find a global optimal solution equipped with kernel functions. This research trains and tests the SVM network and constructs the support vector regression prediction model by using hydrologic data. Six hydrologic time series were calculated by different kernel functions (namely, linear, polynomial, radial basis function (RBF)), to determine which kernel is the more suitable hydrologic time series in practice. A new solution is presented to identify the good parameter (C; g) by using grid-search and cross-validation. Results prove that linear SVM is a superior model to polynomial and RBF and produced the most accurate results for modeling hydrologic time series behavior as complex hydrologic phenomena. The case study also shows that the calculation errors were correlated with data characteristics. More stable raw data will result in a more accurate result, whereas more random data will result in a more inaccurate result. Model performance could also be dependent on base data nonlinearity.

scholarly journals High Performance of Polynomial Kernel at SVM Algorithm for Sentiment Analysis

2018 ◽  
Vol 3 (2) ◽  
pp. 194
Author(s):  
Lailil Muflikhah ◽  
Dimas Joko Haryanto
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
High Performance ◽  
Kernel Functions ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Svm Algorithm ◽  
Rbf Kernel ◽  
C Value ◽  
Parameter Values

Sentiment analysis is a text mining based on the opinion collection towards the review of online product. Support Vector Machine (SVM) is an algorithm of classification that applicable to review the analysis of product. The hyperplane kernel function of SVM has importance role to classify the certain category. Therefore, this research is address to investigate the performance between Polynomial and Radial Basis Function (RBF) kernel functions for sentiment analysis of review product. They are examined to 200 comments using 10-fold validation and various parameter values (learning rate, lambda, c value, epsilon and iteration). As general, the performance for polynomial kernel of 88.75% is slightly higher than RBF kernel of 83.25%.

scholarly journals Performance comparison of support vector machine (SVM) with linear kernel and polynomial kernel for multiclass sentiment analysis on twitter

2021 ◽  
Vol 13 (2) ◽  
pp. 168-174
Author(s):  
Rifqatul Mukarramah ◽  
Dedy Atmajaya ◽  
Lutfi Budi Ilmawan
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Confusion Matrix ◽  
Classification Performance ◽  
Performance Comparison ◽  
Kernel Functions ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Linear Kernel ◽  
Testing Method

Sentiment analysis is a technique to extract information of one’s perception, called sentiment, on an issue or event. This study employs sentiment analysis to classify society’s response on covid-19 virus posted at twitter into 4 polars, namely happy, sad, angry, and scared. Classification technique used is support vector machine (SVM) method which compares the classification performance figure of 2 linear kernel functions, linear and polynomial. There were 400 tweet data used where each sentiment class consists of 100 data. Using the testing method of k-fold cross validation, the result shows the accuracy value of linear kernel function is 0.28 for unigram feature and 0.36 for trigram feature. These figures are lower compared to accuracy value of kernel polynomial with 0.34 and 0.48 for unigram and trigram feature respectively. On the other hand, testing method of confusion matrix suggests the highest performance is obtained by using kernel polynomial with accuracy value of 0.51, precision of 0.43, recall of 0.45, and f-measure of 0.51.

scholarly journals Knots timber detection and classification with C-Support Vector Machine

2019 ◽  
Vol 8 (1) ◽  
pp. 246-252
Author(s):  
Fakhira Iwani Muhammad Redzuan ◽  
Marina Yusoff
Keyword(s):  
Support Vector Machine ◽  
Hevea Brasiliensis ◽  
Promising Result ◽  
Extraction Methods ◽  
Kernel Functions ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Linear Kernel ◽  
Different Types ◽  
Contrast Stretching

Timber knots recognition is of prime importance to further determine the timber grade. The recognition is normally based on the human expert’s eyes in which can lead to some flaws based on human limitations and weaknesses. The use of X-ray can cause emits radiation and can be dangerous to the workers. This paper addresses the employment of computational methods for knot detection. A pre-processing and feature extraction methods include contrast stretching, median blur and thresholding, gray scale and local binary pattern were used. More than 400 datasets of knot images of the tropical timbers, namely Acacia and Hevea Brasiliensis have been tested using C-support vector machine as a knot classifier. The findings demonstrate different performances for three types of kernel. Linear kernel function outperformed both radial basis function and polynomial kernel functions for Acacia and Hevea Brasiliensis species. Both species classifications using linear kernel have managed to achieve a promising accuracy. Knots classification with the used of support vector machine has shown a promising result to improve the classifier and test with different types of tropical timbers.

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