scholarly journals Comparison of Kernel Function on Support Vector Machine in Classification of Childbirth

2019 ◽  
Vol 5 (2) ◽  
pp. 90-99
Author(s):  
Putroue Keumala Intan
Keyword(s):  
Support Vector Machine ◽  
Kernel Function ◽  
Kernel Functions ◽  
Support Vector ◽  
Medical Team ◽  
Classification Methods ◽  
Higher Dimension ◽  
Linear Kernel ◽  
Good Decision

The maternal mortality rate during childbirth can be reduced through the efforts of the medical team in determining the childbirth process that must be undertaken immediately. Machine learning in terms of classifying childbirth can be a solution for the medical team in determining the childbirth process. One of the classification methods that can be used is the Support Vector Machine (SVM) method which is able to determine a hyperplane that will form a good decision boundary so that it is able to classify data appropriately. In SVM, there is a kernel function that is useful for solving non-linear classification cases by transforming data to a higher dimension. In this study, four kernel functions will be used; Linear, Radial Basis Function (RBF), Polynomial, and Sigmoid in the classification process of childbirth in order to determine the kernel function that is capable of producing the highest accuracy value. Based on research that has been done, it is obtained that the accuracy value generated by SVM with linear kernel functions is higher than the other kernel functions.

scholarly journals Multi-class classification for large datasets with optimized SVM by non-linear kernel function

2021 ◽  
Vol 2089 (1) ◽  
pp. 012015
Author(s):  
Lingam Sunitha ◽  
M Bal Raju
Keyword(s):  
Kernel Function ◽  
Kernel Functions ◽  
Support Vector ◽  
Data Sets ◽  
Classification Methods ◽  
Linear Kernel ◽  
Vector Machines ◽  
Multi Class Classification ◽  
The One ◽  
Work First

Abstract Most important part of Support Vector Machines(SVM) are the kernels. Although there are several widely used kernel functions, a carefully designed kernel will help improve the accuracy of SVM. The proposed work aims to develop a new kernel function for a multi-class support vector machine, perform experiments on various data sets, and compare them with other classification methods. Directly it is not possible multiclass classification with SVM. In this proposed work first designed a model for binary class then extended with the one-verses-all approach. Experimental results have proved the efficiency of the new kernel function. The proposed kernel reduces misclassification and time. Other classification methods observed better results for some data sets collected from the UCI repository.

Image Normalization and Weighted Classification Using an Efficient Approach for SVM Classifiers

2020 ◽  
Vol 20 (04) ◽  
pp. 2050035
Author(s):  
Sumit Dhariwal ◽  
Sellappan Palaniappan
Keyword(s):  
Support Vector Machine ◽  
Image Classification ◽  
Error Rate ◽  
Kernel Functions ◽  
Support Vector ◽  
Image Normalization ◽  
Novel Approach ◽  
Low Levels ◽  
To Come

The content of massive image changing the brightest brightness is an impasse between most tests of sorted image realizations with low-resolution representation. I have done this research through image security, which will help curb crime in the coming days, and we propose a novel receipt for their strong and effective counterpart. Image classification using low levels of the image is a difficult method, so for this, I have adopted the method of automating the semantic image classification of this research and used it with different SVM classifiers, based on the normalized weighted feature support vector machine for semantic image classification. This is a novel approach given that weighted feature or normalized biased feature is applied and it is found that the normalized method is the best. It also uses normalized weighted features to compute kernel functions and train SVM. The trained SVM is then used to classify new images. During training and generalization, we displayed a decrease of identification error rate and there have been many benefits of using SVM with better performance in normalized image-cataloging systems. The importance of this technique and its role will be highlighted in the years to come.

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.

Kernel Parameter Selection for SVM Classification

2011 ◽  
pp. 44-55
Author(s):  
Manju Bala ◽  
R. K. Agrawal
Keyword(s):  
Support Vector Machine ◽  
Kernel Function ◽  
Classification Accuracy ◽  
Parameter Selection ◽  
Kernel Functions ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Svm Classification ◽  
Kernel Parameter ◽  
Benchmark Datasets

The choice of kernel function and its parameter is very important for better performance of support vector machine. In this chapter, the authors proposed few new kernel functions which satisfy the Mercer’s conditions and a robust algorithm to automatically determine the suitable kernel function and its parameters based on AdaBoost to improve the performance of support vector machine. The performance of proposed algorithm is evaluated on several benchmark datasets from UCI repository. The experimental results for different datasets show that the Gaussian kernel is not always the best choice to achieve high generalization of support vector machine classifier. However, with the proper choice of kernel function and its parameters using proposed algorithm, it is possible to achieve maximum classification accuracy for all datasets.

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.

Spectral Classification of Oral and Nasal Snoring Sounds Using a Support Vector Machine

2013 ◽  
Vol 17 (4) ◽  
pp. 611-621 ◽  
Author(s):  
Tsuyoshi Mikami ◽  
◽  
Yohichiro Kojima ◽  
Kazuya Yonezawa ◽  
Masahito Yamamoto ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Upper Airway ◽  
Kernel Functions ◽  
Support Vector ◽  
Svm Classifier ◽  
Spectral Classification ◽  
Simple Method ◽  
Oral Breathing ◽  
Sleep Condition

Since oral breathing during sleep tends to make the upper airway more collapsible, loud snoring caused by oral breathing is found in many sleep apnea/hypopnea patients and should be detected in the earlier stage. But unfortunately we cannot know our own sleep condition or snoring. Thus, a simple method that can detect oral snoring makes it possible to become a useful technique to develop a home medical device. For such purpose, we adopt a Support Vector Machine (SVM) classifier so as to classify oral and nasal snoring sounds based on the spectral properties. Fifteen subjects are asked to simulate snoring with oral and nasal breath respectively and the sounds are recorded with a linear sound recorder. We adopted seven kernel functions (linear, polynomial, sigmoid, Gaussian, Laplacian, chisquare, and Kullback-Leibler) for SVM-based spectral classification. As a result, over 95% of snoring sounds are successfully classified under the various cross validation test.

scholarly journals Estimation of Heart Rate from Vocal Frequency Based on Support Vector Machine

2016 ◽  
Vol 2 (1) ◽  
pp. 16 ◽  
Author(s):  
Motoki Sakai
Keyword(s):  
Heart Rate ◽  
Support Vector Machine ◽  
Kernel Function ◽  
Prediction Models ◽  
Kernel Functions ◽  
Inner Function ◽  
Support Vector ◽  
Hyperbolic Tangent ◽  
Hyperbolic Tangent Function ◽  
Tangent Function

Heart rate (HR) is one of the vital signs used to assess our physical condition; it would be beneficial if HR could easily be obtained without special medical instruments. In this study, a feature of vocal frequency was used to estimate HR, because it can easily be recorded with a common device such as a smartphone. Previous studies proposed that a support vector machine (SVM) that adopted the inner product as the kernel function was efficient for estimating HR to a certain extent. However, these studies did not present the effectiveness of other kernel functions, such as the hyperbolic tangent function. Therefore, this study identified a combination of kernel functions of the kernel ridge regression (KRR). In addition, features of vocal frequency to effectively estimate HR were investigated. To evaluate the effectiveness, experiments were conducted with two subjects. In the experiment, 60 sets of HRs and voice data were measured per subject. To identify the most effective kernel function, four kernel functions (the inner function, Gaussian function, polynomial function, and hyperbolic tangent function) were compared. Moreover, effective features of vocal frequency were selected with the sequential feature selection (SFS) method. As a consequence, the hyperbolic tangent function worked best, and high-frequency components of voice were efficient. However, results of this research indicated that effective vocal spectrum components to estimate HR differ depending on prediction models.

The Research of Face Recognition Based on Kernel Function

2013 ◽  
Vol 321-324 ◽  
pp. 1181-1185
Author(s):  
Yong Jun Zhu ◽  
Wen Bo Liu ◽  
Feng Yu Jin ◽  
Yin Wu
Keyword(s):  
Support Vector Machine ◽  
Kernel Function ◽  
Feature Space ◽  
Kernel Functions ◽  
Weighting Coefficient ◽  
Support Vector ◽  
New Learning ◽  
Learning Machine ◽  
Matrix Base ◽  
Selection Of

The way of kernel function has been widely applied in machine learning field, such as artificial neural network and support vector machine, for avoiding dimensional disaster of feature space and improving performance of learning machine effectively. The selection of kernel function and construction of new kernel are the core problems, which have a direct relation with the performance of classification, and the research of this field is not enough. In this paper support vector machine (SVM) was used as an example, and the performance of common kernel functions was evaluated through observing and computing the features of kernel matrix. Base on this, a new mixed kernel function was gotten by optimization of kernel functions, and the experimental data proved that the performance of SVM was improved by the mixed kernel function. If the weighting coefficient was selected properly, the correct rate could even reach to 100%. What’s more, not only a method to construct a new learning machine was given, but also a reference for selecting kernel function was given.

Fault Diagnosis of Conveyance Machine Based on Fuzzy Support Vector

2011 ◽  
Vol 135-136 ◽  
pp. 547-552
Author(s):  
Yuan Bin Hou ◽  
Ning Li ◽  
Fan Guo ◽  
Jing Chen
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Kernel Function ◽  
Coal Mine ◽  
Fuzzy Theory ◽  
Kernel Functions ◽  
Mine Safety ◽  
Support Vector ◽  
Coal Mine Safety ◽  
Rubber Belt

Aiming random and nonlinearity for conveyance machine of rubber belt in mine, a method of fault diagnosis is presented which fusion of fuzzy theory and support vector machine (FSVM). According to the coal mine safety rules of the regulation, the conveyance machine servicing are deduced eleven faults after analyzing practice statistic data, here, we consider some are fuzzy that the statistic data are divided to the normal kind or fault kind, but some are definite that the statistic data possibility are belong to same kind fault, accordingly, the fuzzy support vectors is established. Farther, two kernel functions of FSVM is made for seeking the problem of random and nonlinearity, which are RBF and TANH. According to the random statistic data and the study sample, analyzing the effect of expense and kernel function in selecting different parameters, the unitary constant is ascertained, next, the FSVM kernel function of fault diagnosis multi-class rules are established, then, this method availability is proved using test data and simulation.

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