Research Survey on Support Vector Machine

Wang, Huibing; Xiong, Jinbo; Yao, Zhiqiang; Lin, Mingwei; Ren, Jian

doi:10.4108/eai.13-7-2017.2270596

Cited by 22 publications

(12 citation statements)

References 58 publications

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“…Here, a supervised machine learning model called support vector regression (SVR) was trained with input and output from several IAMs. SVR (Appendix D) has good learning performance for prediction even on relatively small datasets because of its generalizable structural error minimization method, and it is effective with high‐dimensional data (Moura et al, 2011; Pedregosa et al, 2011; Wang et al, 2017).…”

Section: Alternative Approach To Estimate Social Cost Of Carbon With ...mentioning

confidence: 99%

An alternative approach to capture uncertainties embedded in the estimation of social cost of carbon

Caesary

Kim

Nam

2023

WIREs Energy & Environment

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The continuous increase in global surface temperature, which has been triggered by the increase in atmospheric CO2 concentration from anthropogenic activity, results in damage not only in the short term but also in the long term. This damage, called the social cost of carbon (SCC), has been widely estimated using integrated assessment models (IAMs). A large range of estimated SCC values have been observed because of uncertainties in IAMs' parameters. This study provides a comprehensive review of these uncertainties after dividing IAM modules into four categories: climate sensitivity, damage function, discount rate, and regional–sectoral validation. The review was conducted by comparing key ideas considered by various IAMs: socioeconomic conditions in relation to projected CO2 emissions, estimation of the atmospheric concentration of CO2, estimation of total radiative forcing, parameters of the temperature function, parameters of the damage function, and discount rate value. In addition, this study presents an alternative approach to capture the uncertainties embedded in the SCC estimation, using a machine learning approach. This enables a probabilistic evaluation of a specific level of SCC and improves our comprehension of the implication of the calculated SCC using IAMs. This alternative approach provides a basis for further study of SCC. This article is categorized under: Climate and Environment > Net Zero Planning and Decarbonization

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Section: Alternative Approach To Estimate Social Cost Of Carbon With ...mentioning

confidence: 99%

An alternative approach to capture uncertainties embedded in the estimation of social cost of carbon

Caesary

Kim

Nam

2023

WIREs Energy & Environment

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“…Wang et al [7] done a research survey on SVM. Analysed the equation for quadrature problems, studied parameter optimization for SVM.…”

Section: Literature Surveymentioning

confidence: 99%

Performance Comparison between SVM and LS-SVM for Rice Leaf Disease detection

Acharya,

Kar,

Samal

et al. 2023

ICST Transactions on Scalable Information Systems

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INTRODUCTION: Automatic detection of rice plant diseases at early stage from its images is quite beneficial over traditional verification methods. OBJECTIVES: Recent years machine learning (ML) approaches are more efficient in disease classification task. In current generation the statistical machine learning algorithm which shows state-of-arts performance is Support Vector Machine (SVM) and variants of SVM. METHODS: SVM has an excellent learning performance for linear and non-linear data samples. It works for Quadratic Programming Problems (QPP) due to which it has the drawback of computational complexity. However QPP can be solved linearly with the help of Least Square SVM(LS-SVM) approach. In LS-SVM the epsilon tube and slack variables of SVM are replaced with error variables. The distance is calculated by error square value. RESULTS: In this research performance comparison is made between SVM and LS-SVM for rice leaf diseases such as Bacterial Leaf Blight (BLB), Brown spot(BS), Leaf smut(LS) and Leaf Blast using two datasets (DS1 and DS2).Accuracy of LS-SVM is found to be 91.3% and 98.87% for DS1 and DS2 respectively whereas accuracy of SVM is 83.3% and 98.75% for DS1 and DS2 respectively. CONCLUSION: Performance of LS-SVM outperformed than SVM in terms of accuracy.

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“…Support vector machines (SVMs) are among the best-performing machine learning algorithms which give highly accurate results 10 . SVMs have a good learning ability and are generally good at classification, regression and forecasting 31 . The foundations of SVMs have been developed by Vapnik 30 and gained popularity due to many promising features, such as better empirical performance.…”

Section: Introductionmentioning

confidence: 99%