Fuzzy Support Vector Machines for Face Recognition: A Review

Prakash, N. Krishna; Singh, Yashpal

doi:10.5120/ijca2015907224

Cited by 10 publications

(7 citation statements)

References 14 publications

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“…Nowadays, fuzzy support vector machines have been received considerable attention [23][24][25]. The fuzzy support vector machine is an improved support vector machine.…”

Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Fuzzy reliability analysis based on support vector machine

Nie

Chen

et al. 2021

Preprint

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For a structure with implicit performance function structure and less sample data, it is difficult to obtain accurate probability distribution parameters by traditional statistical analysis methods. To address the issue, the probability distribution parameters of samples are often regarded as fuzzy numbers. In this paper, a novel fuzzy reliability analysis method based on support vector machine is proposed. Firstly, the fuzzy variable is converted into an equivalent random variable, and the equivalent mean and equivalent standard deviation are calculated. Secondly, the support vector regression machine with excellent small sample learning ability is used to train the sample data. Subsequently, and the performance function is approximated. Finally, the Monte Carlo method is used to obtain fuzzy reliability. Numerical examples are investigated to demonstrate the effectiveness of the proposed method, which provides a feasible way for fuzzy reliability analysis problems of small sample data.

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“…Nowadays, fuzzy support vector machines have been received considerable attention [23][24][25]. The fuzzy support vector machine is an improved support vector machine.…”

Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Fuzzy reliability analysis based on support vector machine

Nie

Chen

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…It is suggested that the Levenberg-Marquardt algorithm should be used first, followed by BFGS algorithm or conjugate gradient method and RPROP algorithm. In Lixia, Ye Jiaan [17] et al proposed a cellular automata based on neural network and used it to simulate the complex land use system and its evolution. There are many studies on urban simulation using cellular automata in the world, but these models are often limited to simulating the transformation from Non-urban land to urban land.…”

Section: Introductionmentioning

confidence: 99%

Retracted: Image Recognition Technology Based on Neural Network

Chen

2020

IEEE Access

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Facial expression recognition is an important part of human-computer interaction. Using deep learning algorithms to recognize and classify facial expressions has become a hot issue for scholars from all walks of life. In this paper, the traditional classification algorithm based on the convolutional neural network is improved, and the feature information of the key parts of the face is used to integrate the key part features with the global features of the face image to better distinguish similar categories. Therefore, this paper designs a method to locate the key points of the face image and optimizes the key point positioning method through multiple experiments to facilitate the extraction of the feature information of the key points. For the calculation of classification results, a multi-region test method is used. By calculating multiple regions of the image during the test, the accuracy of image recognition can be improved. The final experimental results show that the model with key point feature information has more advantages in accuracy, and the robustness of the model is improved.

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“…This study mainly analyzed SVM. As a common classification and prediction algorithm, SVM has a good application in many fields, such as face recognition [22], risk assessment [23], electricity price prediction [24] and image classification [25].…”

Section: Discussionmentioning

confidence: 99%

Research on the Detection of Network Intrusion Prevention With Svm Based Optimization Algorithm

Wang¹,

Xu²

2020

IJCAI

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Support vector machine (SVM) has a good application in intrusion detection, but its performance needs to be further improved. This study mainly analyzed the SVM optimization algorithm. The principle of SVM was introduced firstly, then SVM was improved using the improved whale optimization algorithm (WOA), the improved WOA (IWOA)-SVM based intrusion detection method was analyzed, and finally experiments were carried out on KDD CUP99 to verify the effectiveness of the algorithm. The results showed that the IWAO-SVM algorithm was more accurate in attack detection; compared with SVM, PSO-SVM and ant colony optimization (ACO)-SVM algorithms, the performance of the IWAO-SVM algorithm was better, the detection rate was 99.89%, the precision ratio was 99.92%, the accuracy rate was 99.86%, and the detection time was 192 s, showing that it had high precision in intrusion detection. The experimental results verify the reliability of the IWAO-SVM algorithm, and it can be promoted and applied in the detection of network intrusion prevention.Povzetek: Algoritem SVM je bil prilagojen za iskanje napadov v omrežjih.

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Fuzzy Support Vector Machines for Face Recognition: A Review

Cited by 10 publications

References 14 publications

WITHDRAWN: Fuzzy reliability analysis based on support vector machine

WITHDRAWN: Fuzzy reliability analysis based on support vector machine

Retracted: Image Recognition Technology Based on Neural Network

Research on the Detection of Network Intrusion Prevention With Svm Based Optimization Algorithm

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