Two smooth support vector machines for 
                $$\varepsilon $$
                
                    
                        ε
                    
                
            -insensitive regression

Gu, Wei-Zhe; Chen, Wei Po; Ko, Chun-Hsu; Lee, Yuh Jye; Chen, Jein Shan

doi:10.1007/s10589-017-9975-9

Cited by 9 publications

(2 citation statements)

References 13 publications

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“…In the advancement of traditional SVM, the kernel tricks concept is used to handle non-linear data, whereas the general SVM model can be applied to linearly separable datasets [4]- [6], [10]. Kernel Tricks like Radial Basis function (RBF), Sigmoid and Linear model are adopted [17]. The RBF kernel with regularization parameter (C) and gamma as "scale" (Kernel coefficient) gave the best performance in comparison with linear and sigmoid kernel tricks, as given in Table 5.…”

Section: Support Vector Machinementioning

confidence: 99%

A Machine Learning-based Diagnosis and Prediction of Diabetes Mellitus Disease

Thotad

2023

Preprint

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Traditional diagnostic techniques are prone to human error and time consuming. Computer-aided diagnostic techniques improve the performance and reduce the expenses. This paper presents machine learning based classifiers to detect diabetes in India and Indian Demographic & Health Survey (2019–21) dataset is considered for the analysis. Classifiers like Support Vector Machine, Decision Tree, Extreme Gradient Boosting, and Random Forest are considered. Principal Component Analysis is used for feature reduction. Hyperparameters are tuned to achieve good performance of the classifiers. The four phases adopted in the work include preprocessing, feature extraction, classification, and analysis. From the results, the Random Forest has given the maximum classification accuracy, precision, recall, and area under the curve in comparison with other models. The work finds application in healthcare for predictive analysis of diabetes.

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Section: Support Vector Machinementioning

confidence: 99%

A Machine Learning-based Diagnosis and Prediction of Diabetes Mellitus Disease

Thotad

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In the advancement of traditional SVM, the kernel tricks concept is used to handle non-linear data, whereas the general SVM model can be applied to linearly separable datasets [4]- [6], [10]. Kernel Tricks like the Radial Basis function (RBF), Sigmoid, and Linear model are adopted [17]. The RBF kernel with regularization parameter (C) and gamma as "scale" (Kernel coefficient) gave the best performance in comparison with linear and sigmoid kernel tricks, as given in Table 5.…”

Section: Support Vector Machinementioning

confidence: 99%

A Machine Learning-based Diagnosis and Prediction of Diabetes Mellitus Disease

Thotad

2023

Preprint

View full text Add to dashboard Cite

Traditional diagnostic techniques are prone to human error and are time-consuming. Computer-aided diagnostic procedures improve performance and reduce expenses. This paper presents machine learning-based classifiers to detect diabetes in India, and Indian Demographic & Health Survey (2019–21) dataset is considered for the analysis. Classifiers like Support Vector Machine, Decision Tree, Extreme Gradient Boosting, and Random Forest are considered. The four phases adopted in work include preprocessing, feature extraction, classification, and analysis. Principal Component Analysis is used for feature reduction. Hyper-tuning parameters are tuned to achieve good performance of the classifiers. From the results, Random Forest has given the maximum classification accuracy, precision, recall, and area under the curve compared with other models. The work finds application in healthcare for the predictive analysis of diabetes.

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Parameter Inverse Identification for Direct Laser Deposition Simulation Using Support Vector Regression and Genetic Algorithm

Chen

Hou

et al. 2022

Mechanisms and Machine Science

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Two smooth support vector machines for $$\varepsilon $$ ε -insensitive regression

Cited by 9 publications

References 13 publications

A Machine Learning-based Diagnosis and Prediction of Diabetes Mellitus Disease

A Machine Learning-based Diagnosis and Prediction of Diabetes Mellitus Disease

A Machine Learning-based Diagnosis and Prediction of Diabetes Mellitus Disease

Parameter Inverse Identification for Direct Laser Deposition Simulation Using Support Vector Regression and Genetic Algorithm

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