Twin support vector machine: theory, algorithm and applications

Ding, Shifei; Zhang, Nan; Zhang, Xiekai; Wu, Fulin

doi:10.1007/s00521-016-2245-4

Cited by 90 publications

(23 citation statements)

References 52 publications

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“…Referred to relevant algorithms (Deng and Tian, ), an optimization problem can be constructed and solved according to the least square support vector regression algorithm of homogeneous decision function. For the homogeneous Equation , the hyper plane can be written as w · x = 0 where w is the slope of the regression hyper plane and the classification interval is 2/‖ w ‖ 2 .…”

Section: A Svm‐based Vvp Methodsmentioning

confidence: 99%

A support vector machine‐based VVP wind retrieval method

Wei

et al. 2015

Atmospheric Science Letters

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Velocity volume processing (VVP) is a common wind retrieval method for single-Doppler radar. However, hindered by the problem of the ill-conditioned coefficient matrix, VVP method has not been applied effectively. In this paper, a support vector machine-based VVP (SVM VVP) method is proposed to improve the VVP method. SVM VVP retrieves wind field through constructing a support vector regression machine for the radial velocity. The new method is applied to a real case, and the retrieval shows that SVM VVP can give accurate and high-resolution results. It is helpful for studying wind fields of small-scale precipitation systems.

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Section: A Svm‐based Vvp Methodsmentioning

confidence: 99%

A support vector machine‐based VVP wind retrieval method

Wei

et al. 2015

Atmospheric Science Letters

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“…In this section, related works including interpolation method and modeling of air quality will be discussed. With the development of hardware equipment, the researches in air quality modeling showed an increasing trend and the focus of research has changed from traditional machine learning including support vector machine (SVM) and decision tree regression (DTR) to ensemble learning and deep learning [4]- [6]. Recent studies have shown that deep learning performs better than other machine learning methods in air quality modeling [7], [8].…”

Section: Literature Reviewsmentioning

confidence: 99%

“…Similarly, the larger the value of the reset gate, the greater the impact. The formulas of the reset gate and update gate are shown in Equation(6) and(7).…”

mentioning

confidence: 99%

An Unsupervised PM2.5 Estimation Method With Different Spatio-Temporal Resolutions Based on KIDW-TCGRU

et al. 2020

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The interpolation of fine-grained air quality has significant prospects in the area of air quality monitoring. The solution to this problem can effectively monitor the air quality of the areas by sparse air quality monitoring stations, so as to reduce the monitoring cost. Most of the existing researches are to solve the problem of air quality monitoring in the areas without stations by different interpolation methods. However, most of them are unable to verify the reliability of the proposed interpolation methods and can't provide a feasible range of interpolation methods at spatial resolutions. Therefore, this paper proposes an effectively unsupervised PM2.5 estimation method based on time distributed convolutional gated recurrent unit (TCGRU) and an interpolation method based on k-nearest neighbor inverse distance weighted (KIDW) to solve these problems. The model is trained by the data obtained by the interpolation method with missing one station in turn to get the monitoring capability of the areas without monitoring stations. Inverse distance weighted is combined with k-nearest neighbor to improve the performance of interpolation. In addition, time distributed convolutional neural network extracts the spatial features of air quality and storage time information for extracting temporal features by the gated recurrent units. A large number of experiments are carried out to evaluate the performance of the method by using the air quality dataset of Hubei Province, China. The experimental results show that the proposed model is effective for the monitoring of PM2.5 in the unsupervised areas and the performance (i.e. MAE=8.23, RMSE=11.27 and MAPE=19.14%) of estimating PM2.5 concentration is better than the comparative methods. INDEX TERMS Air quality modeling, spatio-temporal analysis, deep learning, inverse distance weighted, k-nearest neighbor.

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“…Finally, the pre-processed training and test data sets were grouped and tested, and experiments with the four classification algorithms were carried out. These were SVM [21][22][23], naive Bayes [24], decision tree [25], and MLP and they were used to train and test the data and compare and analyze the results. Each record had data with 41 different feature attributes presenting the content of the network packets.…”

Section: Materials and Experimental Setupmentioning

confidence: 99%

Detection and Classification of Advanced Persistent Threats and Attacks Using the Support Vector Machine

2019

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Traditional network attack and hacking models are constantly evolving to keep pace with the rapid development of network technology. Advanced persistent threat (APT), usually organized by a hacker group, is a complex and targeted attack method. A long period of strategic planning and information search usually precedes an attack on a specific goal. Focus is on a targeted object and customized specific methods are used to launch the attack and obtain confidential information. This study offers an attack detection system that enables early discovery of the APT attack. The system uses the NSL-KDD database for attack detection and verification. The main method uses principal component analysis (PCA) for feature sampling and the enhancement of detection efficiency. The advantages and disadvantages of using the classifiers are then compared to detect the dataset, the classifier supports the vector machine, naive Bayes classification, the decision tree and neural networks. Results of the experiments show the support vector machine (SVM) to have the highest recognition rate, reaching 97.22% (for the trained subdata A). The purpose of this study was to establish an APT early warning model mechanism, that could be used to reduce the impact and influence of APT attacks.

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Twin support vector machine: theory, algorithm and applications

Cited by 90 publications

References 52 publications

A support vector machine‐based VVP wind retrieval method

A support vector machine‐based VVP wind retrieval method

An Unsupervised PM2.5 Estimation Method With Different Spatio-Temporal Resolutions Based on KIDW-TCGRU

Detection and Classification of Advanced Persistent Threats and Attacks Using the Support Vector Machine

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