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OKO-SVM:Online kernel optimization-based support vector machine for the incremental learning and classification of the sentiments in the train reviews

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International Journal of Modeling, Simulation, and Scientific Computing 2018年第6期9卷 100-126页

作者： Rashmi K.Thakur Manojkumar V.Deshpande MPSTME NMIMS UniversityMumbaiIndia Prestige Institute of Engineering Management&Research Indore India

Online incremental learning is one of the emerging research interests among the researchers in the recent *** sentiment classification through the online incremental learning faces many challenges due to the limitations in the memory and the computing resources available for processing the online *** work has introduced an online incremental learning algorithm for classifying the train *** sentiments available in the reviews provided for the public services are necessary for improving the quality of the *** work proposes the online kernel optimizationbased support vector machine(OKO-SVM)classifier for the sentiment classification of the train *** paper is the extension of the previous work kernel optimizationbased support vector machine(KO-SVM).The OKO-SVM classifier uses the proposed fuzzy bound for modifying the weight for each incoming review database for the particular time *** simulation uses the standard train review and the movie review database for the *** the simulation results,it is evident that the proposed model has achieved a better performance with the values of 84.42%,93.86%,and 74.56%regarding the accuracy,sensitivity,and specificity while classifying the train review database.

关键词： Online incremental learning train reviews sentiment classification kernel optimization train review database.

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kernel-based adversarial attacks and defenses on support vector classification

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Digital Communications and Networks 2022年第4期8卷 492-497页

作者： Wanman Li Xiaozhang Liu Anli Yan Jie Yang School of Computer Science and Technology Hainan UniversityHaikou570228China

While malicious samples are widely found in many application fields of machine learning,suitable countermeasures have been investigated in the field of adversarial machine *** to the importance and popularity of Support Vector Machines(SVMs),we first describe the evasion attack against SVM classification and then propose a defense strategy in this *** evasion attack utilizes the classification surface of SVM to iteratively find the minimal perturbations that mislead the nonlinear ***,we propose what is called a vulnerability function to measure the vulnerability of the SVM *** this vulnerability function,we put forward an effective defense strategy based on the kernel optimization of SVMs with Gaussian kernel against the evasion *** defense method is verified to be very effective on the benchmark datasets,and the SVM classifier becomes more robust after using our kernel optimization scheme.

关键词： Adversarial machine learning Support vector machines Evasion attack Vulnerability function kernel optimization

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