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Journal articles on the topic 'Support vector data description'

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Published: 4 June 2021
Last updated: 16 June 2025

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1

Tax, David M. J., and Robert P. W. Duin. "Support Vector Data Description." Machine Learning 54, no. 1 (2004): 45–66. http://dx.doi.org/10.1023/b:mach.0000008084.60811.49.

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2

Sadeghi, Reza, and Javad Hamidzadeh. "Automatic support vector data description." Soft Computing 22, no. 1 (2016): 147–58. http://dx.doi.org/10.1007/s00500-016-2317-5.

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3

Teeyapan, Kasemsit, Nipon Theera-Umpon, and Sansanee Auephanwiriyakul. "Ellipsoidal support vector data description." Neural Computing and Applications 28, S1 (2016): 337–47. http://dx.doi.org/10.1007/s00521-016-2343-3.

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4

Gorgani, Mohammad Ebrahim, Mahdi Moradi, and Hadi Sadoghi Yazdi. "An Empirical Modeling of Companies Using Support Vector Data Description." International Journal of Trade, Economics and Finance 1, no. 2 (2010): 221–24. http://dx.doi.org/10.7763/ijtef.2010.v1.41.

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5

WANG, Xiao-ming, Shi-tong WANG, and Hong PENG. "Minimum variance support vector data description." Journal of Computer Applications 32, no. 2 (2013): 416–18. http://dx.doi.org/10.3724/sp.j.1087.2012.00416.

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6

Rahimzadeh Arashloo, Shervin. "ℓ -Norm Support Vector Data Description". Pattern Recognition 132 (грудень 2022): 108930. http://dx.doi.org/10.1016/j.patcog.2022.108930.

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7

Sohrab, Fahad, Jenni Raitoharju, Alexandros Iosifidis, and Moncef Gabbouj. "Ellipsoidal Subspace Support Vector Data Description." IEEE Access 8 (2020): 122013–25. http://dx.doi.org/10.1109/access.2020.3007123.

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8

Huang, Guangxin, Huafu Chen, Zhongli Zhou, Feng Yin, and Ke Guo. "Two-class support vector data description." Pattern Recognition 44, no. 2 (2011): 320–29. http://dx.doi.org/10.1016/j.patcog.2010.08.025.

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9

Sohrab, Fahad, Jenni Raitoharju, Alexandros Iosifidis, and Moncef Gabbouj. "Multimodal subspace support vector data description." Pattern Recognition 110 (February 2021): 107648. http://dx.doi.org/10.1016/j.patcog.2020.107648.

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10

Cha, Myungraee, Jun Seok Kim, and Jun-Geol Baek. "Density weighted support vector data description." Expert Systems with Applications 41, no. 7 (2014): 3343–50. http://dx.doi.org/10.1016/j.eswa.2013.11.025.

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11

Ling, Ping, Xiangyang You, Dajin Gao, Tao Gao, and Xue Li. "Fast distant support vector data description." Memetic Computing 9, no. 1 (2016): 3–14. http://dx.doi.org/10.1007/s12293-016-0189-y.

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12

Lee, KiYoung, Dae-Won Kim, Kwang H. Lee, and Doheon Lee. "Density-Induced Support Vector Data Description." IEEE Transactions on Neural Networks 18, no. 1 (2007): 284–89. http://dx.doi.org/10.1109/tnn.2006.884673.

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13

Zhang, Wenbo. "Support vector data description using privileged information." Electronics Letters 51, no. 14 (2015): 1075–76. http://dx.doi.org/10.1049/el.2014.4483.

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14

Kim, Sangwook, Yonghwa Choi, and Minho Lee. "Deep learning with support vector data description." Neurocomputing 165 (October 2015): 111–17. http://dx.doi.org/10.1016/j.neucom.2014.09.086.

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15

Rahmanimanesh, M., J. A. Nasiri, S. Jalili, and N. Moghaddam Charkari. "Adaptive three-phase support vector data description." Pattern Analysis and Applications 22, no. 2 (2017): 491–504. http://dx.doi.org/10.1007/s10044-017-0646-3.

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16

Rahmanimanesh, Mohammad, Jalal al-Din Nasiri, Saeid Jalili, and Charkari Nasrolah Moghaddam. "Adaptive three‑phase support vector data description." Pattern Analysis and Applications 22, no. 2 (2019): 491–504. https://doi.org/10.1007/s10044-017-0646-3.

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We add a new phase, called reforming phase, to support vector data description (SVDD) between the training and testing phases.‎ The reforming phase enables us to reconsider the SVDD’s assumption of the uniformity of features in calculating the distance of an object to the center of hypersphere.‎ In the reforming phase, the features are assumed as a group of experts who have different impacts in overall outlier detection.‎ In doing so, the proportion of each feature in the distance of an object to the center of hypersphere is specified.‎ Subsequently, the opinions of the experts about the label of the corresponding object are determined based on these measured proportions.‎ By using different group decision-making methods for aggregating the opinions of the experts, a large variety of new models are obtained based on one SVDD’s trained model.‎ Specially, we utilize a kind of ordered weighted averaging operator as group decision-making method and introduce cDFS-SVDD based on this method.‎ cDFS-SVDD performs runtime feature selection and calculates the distance of an object to the center of hypersphere dynamically at test time based on these selected features.‎ We apply the method to the anomaly detection problem in mobile ad hoc networks as well as two UCI datasets by which the performance of SVDD improves significantly in separating the target and outlier objects.‎
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17

Turkoz, Mehmet, Sangahn Kim, Youngdoo Son, Myong K. Jeong, and Elsayed A. Elsayed. "Generalized support vector data description for anomaly detection." Pattern Recognition 100 (April 2020): 107119. http://dx.doi.org/10.1016/j.patcog.2019.107119.

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18

Zhang, Li, and Xingning Lu. "Feature Extraction Based on Support Vector Data Description." Neural Processing Letters 49, no. 2 (2018): 643–59. http://dx.doi.org/10.1007/s11063-018-9838-0.

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19

Phaladiganon, Poovich, Seoung Bum Kim, and Victoria C. P. Chen. "A Density-focused Support Vector Data Description Method." Quality and Reliability Engineering International 30, no. 6 (2014): 879–90. http://dx.doi.org/10.1002/qre.1688.

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20

SUN, Wenzhu, Jianling QU, Yang CHEN, Yazhou DI, and Feng GAO. "Heuristic sample reduction method for support vector data description." TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 24 (2016): 298–312. http://dx.doi.org/10.3906/elk-1307-137.

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21

Li, Chang Zheng, and Yong Lei. "Compressor Surge Detection Based on Support Vector Data Description." Applied Mechanics and Materials 152-154 (January 2012): 1545–49. http://dx.doi.org/10.4028/www.scientific.net/amm.152-154.1545.

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Axial flow compressors work as an indispensable device in industry fields. Surge is a phenomenon of aerodynamic instability, which characterized by disruption of flow. When a compressor works in surge state, the vibration is so intense that it may causes accidents. Detecting surge timely and accurately not only insure safety of compressors but also is a key of active control of aerodynamic instability. Support vector data description (SVDD) is a one-class classification method developed based on the theory of support vector machine (SVM). In this paper, we introduce SVDD into the field of compressor surge detection. It demonstrates that SVDD method can give a warning far ahead of surge.
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22

Guo, Wei, Zhe Wang, Sisi Hong, Dongdong Li, Hai Yang, and Wen Du. "Multi-kernel Support Vector Data Description with boundary information." Engineering Applications of Artificial Intelligence 102 (June 2021): 104254. http://dx.doi.org/10.1016/j.engappai.2021.104254.

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23

Liu, Yi-Hung, Yung Ting, Shian-Shing Shyu, Chang-Kuo Chen, Chung-Lin Lee, and Mu-Der Jeng. "A Support Vector Data Description Committee for Face Detection." Mathematical Problems in Engineering 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/478482.

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Face detection is a crucial prestage for face recognition and is often treated as a binary (face and nonface) classification problem. While this strategy is simple to implement, face detection accuracy would drop when nonface training patterns are undersampled. To avoid these problems, we propose in this paper a one-class learning-based face detector called support vector data description (SVDD) committee, which consists of several SVDD members, each of which is trained on a subset of face patterns. Nonfaces are not required in the training of the SVDD committee. Therefore, the face detection accuracy of SVDD committee is independent of the nonface training patterns. Moreover, the proposed SVDD committee is also able to improve generalization ability of the original SVDD when the face data set has a multicluster distribution. Experiments carried out on the extended MIT face data set show that the proposed SVDD committee can achieve better face detection accuracy than the widely used SVM face detector and performs better than other one-class classifiers, including the original SVDD and the kernel principal component analysis (Kernel PCA).
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24

Mu, T., and A. K. Nandi. "Multiclass Classification Based on Extended Support Vector Data Description." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 39, no. 5 (2009): 1206–16. http://dx.doi.org/10.1109/tsmcb.2009.2013962.

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25

Lv, Zhaomin, and Xuefeng Yan. "Hierarchical Support Vector Data Description for Batch Process Monitoring." Industrial & Engineering Chemistry Research 55, no. 34 (2016): 9205–14. http://dx.doi.org/10.1021/acs.iecr.6b00901.

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26

Wang, Xiaoming, Fu-lai Chung, and Shitong Wang. "Theoretical analysis for solution of support vector data description." Neural Networks 24, no. 4 (2011): 360–69. http://dx.doi.org/10.1016/j.neunet.2011.01.007.

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27

Lee, KiYoung, Dae-Won Kim, Doheon Lee, and Kwang H. Lee. "Improving support vector data description using local density degree." Pattern Recognition 38, no. 10 (2005): 1768–71. http://dx.doi.org/10.1016/j.patcog.2005.03.020.

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28

Hamidzadeh, Javad, and Neda Namaei. "Belief-based chaotic algorithm for support vector data description." Soft Computing 23, no. 12 (2018): 4289–314. http://dx.doi.org/10.1007/s00500-018-3083-3.

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29

A.G., Rekha, Mohammed Shahid Abdulla, and Asharaf S. "Lightly trained support vector data description for novelty detection." Expert Systems with Applications 85 (November 2017): 25–32. http://dx.doi.org/10.1016/j.eswa.2017.05.007.

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30

Zheng, Songfeng. "A fast iterative algorithm for support vector data description." International Journal of Machine Learning and Cybernetics 10, no. 5 (2018): 1173–87. http://dx.doi.org/10.1007/s13042-018-0796-7.

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31

Forghani, Y., H. Sadoghi Yazdi, and S. Effati. "An extension to fuzzy support vector data description (FSVDD*)." Pattern Analysis and Applications 15, no. 3 (2011): 237–47. http://dx.doi.org/10.1007/s10044-011-0208-z.

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32

Wang, Kunzhe, and Haibin Lan. "Robust support vector data description for novelty detection with contaminated data." Engineering Applications of Artificial Intelligence 91 (May 2020): 103554. http://dx.doi.org/10.1016/j.engappai.2020.103554.

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33

Yang, JianXi, Fei Yang, Likai Zhang, et al. "Bridge health anomaly detection using deep support vector data description." Neurocomputing 444 (July 2021): 170–78. http://dx.doi.org/10.1016/j.neucom.2020.08.087.

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34

He, Shu Guang, and Chuan Yan Zhang. "Support Vector Data Description Based Multivariate Cumulative Sum Control Chart." Advanced Materials Research 314-316 (August 2011): 2482–85. http://dx.doi.org/10.4028/www.scientific.net/amr.314-316.2482.

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A SVDD (Support Vector Data Description) based MCUSUM (Multivariate Cumulative Sum) chart is proposed and referred as S-MCUSUM chart, which has an advantage of distribution free. Numerical experiments on the performance of the S-MCUSUM chart is compared to the COT (Cumulative of T) chart. The results show that the COT chart is somewhat better than the S-MCUSUM chart for multivariate normally distributed data. However, the S-MCUSUM chart is much better than the COT chart for banana-shaped distributed data which is a typical non-normal distribution.
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35

Park, Joo-Young, and Dae-Sung Kang. "A Modified Approach to Density-Induced Support Vector Data Description." International Journal of Fuzzy Logic and Intelligent Systems 7, no. 1 (2007): 1–6. http://dx.doi.org/10.5391/ijfis.2007.7.1.001.

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36

Chaudhuri, Arin, Carol Sadek, Deovrat Kakde, et al. "The trace kernel bandwidth criterion for support vector data description." Pattern Recognition 111 (March 2021): 107662. http://dx.doi.org/10.1016/j.patcog.2020.107662.

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37

Ge, Zhiqiang, Furong Gao, and Zhihuan Song. "Batch process monitoring based on support vector data description method." Journal of Process Control 21, no. 6 (2011): 949–59. http://dx.doi.org/10.1016/j.jprocont.2011.02.004.

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38

Lee, Sang-Woong, Jooyoung Park, and Seong-Whan Lee. "Low resolution face recognition based on support vector data description." Pattern Recognition 39, no. 9 (2006): 1809–12. http://dx.doi.org/10.1016/j.patcog.2006.04.033.

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39

Ge, Zhiqiang, and Zhihuan Song. "Bagging support vector data description model for batch process monitoring." Journal of Process Control 23, no. 8 (2013): 1090–96. http://dx.doi.org/10.1016/j.jprocont.2013.06.010.

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40

Hamidzadeh, Javad, Reza Sadeghi, and Neda Namaei. "Weighted support vector data description based on chaotic bat algorithm." Applied Soft Computing 60 (November 2017): 540–51. http://dx.doi.org/10.1016/j.asoc.2017.07.038.

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41

Li, DongDong, Zhe Wang, Chenjie Cao, and Yu Liu. "Information entropy based sample reduction for support vector data description." Applied Soft Computing 71 (October 2018): 1153–60. http://dx.doi.org/10.1016/j.asoc.2018.02.053.

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42

Lee, Seulki, and Seoung Bum Kim. "Time-adaptive support vector data description for nonstationary process monitoring." Engineering Applications of Artificial Intelligence 68 (February 2018): 18–31. http://dx.doi.org/10.1016/j.engappai.2017.10.016.

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43

Tian, Shiwei, Luwen Zhao, and Guangxia Li. "A Support Vector Data Description Approach to NLOS Identification in UWB Positioning." Mathematical Problems in Engineering 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/963418.

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Non-line-of-sight (NLOS) propagation is one of the most important challenges in radio positioning, and, in recent years, significant attention has been drawn to the identification and mitigation of NLOS signals. This paper focuses on the identification of NLOS signals. The authors consider the NLOS identification problem as a one-class classification problem and apply the support vector data description (SVDD), providing accurate data descriptions utilizing kernel techniques, to perform NLOS identification in ultrawide bandwidth (UWB) positioning. Our work is based on the fact that some features extracted from the received signal waveforms, such as the kurtosis, the mean excess delay spread, and the root mean square delay spread, are different between line-of-sight (LOS) and NLOS signals. Numerical simulations are performed to demonstrate the performance, using a dataset derived from a measurement campaign.
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44

Li, Junshi, Caiqian Yang, and Jun Chen. "Sound Damage Detection of Bridge Expansion Joints Using a Support Vector Data Description." Sensors 23, no. 7 (2023): 3564. http://dx.doi.org/10.3390/s23073564.

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A novel method is proposed for the damage identification of modal bridge expansion joints (MBEJs) based on sound signals. Two modal bridge expansion joint specimens were fabricated to simulate healthy and damaged states. A microphone was used to collect the impact signals from different specimens. The wavelet packet energy ratio of the sound signal was used to identify the difference in specimen state. Firstly, the wavelet packet energy ratio was used to establish the feature vectors, which were reduced dimensionality using principal component analysis. Subsequently, a support vector data description model was established to detect the difference in the signals. The identification effects of three parameter optimization methods (particle swarm optimization, genetic algorithm optimization, and Bayesian optimization) were compared. The results showed that the wavelet packet energy ratio of sound signals could effectively distinguish the state of the support bar. The support vector data description of Bayesian optimization worked best, and the proposed method could successfully detect damage to the support bar of MBEJs with an accuracy of 99%.
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45

Chen, Hui, Chao Tan, and Zan Lin. "Authenticity Detection of Black Rice by Near-Infrared Spectroscopy and Support Vector Data Description." International Journal of Analytical Chemistry 2018 (July 9, 2018): 1–8. http://dx.doi.org/10.1155/2018/8032831.

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Black rice is an important rice species in Southeast Asia. It is a common phenomenon to pass low-priced black rice off as high-priced ones for economic benefit, especially in some remote towns. There is increasing need for the development of fast, easy-to-use, and low-cost analytical methods for authenticity detection. The feasibility to utilize near-infrared (NIR) spectroscopy and support vector data description (SVDD) for such a goal is explored. Principal component analysis (PCA) is used for exploratory analysis and feature extraction. Another two data description methods, i.e., k-nearest neighbor data description (KNNDD) and GAUSS method, are used as the reference. A total of 142 samples from three brands were collected for spectral analysis. Each time, the samples of a brand serve as the target class whereas other samples serve as the outlier class. Based on both the first two principal components (PCs) and original variables, three types of data descriptions were constructed. On average, the optimized SVDD model achieves acceptable performance, i.e., a specificity of 100% and a sensitivity of 94.2% on the independent test set with tight boundary. It indicates that SVDD combined with NIR is feasible and effective for authenticity detection of black rice.
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46

于, 向财. "Research of Engine Performance Monitoring Based on Support Vector Data Description." Mechanical Engineering and Technology 11, no. 03 (2022): 292–302. http://dx.doi.org/10.12677/met.2022.113034.

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47

Wang, Bokun, Caiqian Yang, and Yaojing Chen. "Detection Anomaly in Video Based on Deep Support Vector Data Description." Computational Intelligence and Neuroscience 2022 (May 4, 2022): 1–6. http://dx.doi.org/10.1155/2022/5362093.

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Video surveillance systems have been widely deployed in public places such as shopping malls, hospitals, banks, and streets to improve the safety of public life and assets. In most cases, how to detect video abnormal events in a timely and accurate manner is the main goal of social public safety risk prevention and control. Due to the ambiguity of anomaly definition, the scarcity of anomalous data, as well as the complex environmental background and human behavior, video anomaly detection is a major problem in the field of computer vision. Existing anomaly detection methods based on deep learning often use trained networks to extract features. These methods are based on existing network structures, instead of designing networks for the goal of anomaly detection. This paper proposed a method based on Deep Support Vector Data Description (DSVDD). By learning a deep neural network, the input normal sample space can be mapped to the smallest hypersphere. Through DSVDD, not only can the smallest size data hypersphere be found to establish SVDD but also useful data feature representations and normal models can be learned. In the test, the samples mapped inside the hypersphere are judged as normal, while the samples mapped outside the hypersphere are judged as abnormal. The proposed method achieves 86.84% and 73.2% frame-level AUC on the CUHK Avenue and ShanghaiTech Campus datasets, respectively. By comparison, the detection results achieved by the proposed method are better than those achieved by the existing state-of-the-art methods.
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48

Hou, Hui, and Hongquan Ji. "Improved multiclass support vector data description for planetary gearbox fault diagnosis." Control Engineering Practice 114 (September 2021): 104867. http://dx.doi.org/10.1016/j.conengprac.2021.104867.

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49

Li, Kun, Qiang Ling, Yao Qin, et al. "Spectral-Spatial Deep Support Vector Data Description for Hyperspectral Anomaly Detection." IEEE Transactions on Geoscience and Remote Sensing 60 (2022): 1–16. http://dx.doi.org/10.1109/tgrs.2022.3144192.

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50

WANG, Xiao-Ming, and Shi-Tong WANG. "Theoretical Analysis for the Optimization Problem of Support Vector Data Description." Journal of Software 22, no. 7 (2011): 1551–60. http://dx.doi.org/10.3724/sp.j.1001.2011.03856.

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