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Journal articles on the topic 'Support Vector Machine'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Xia, Tian. "Support Vector Machine Based Educational Resources Classification." International Journal of Information and Education Technology 6, no. 11 (2016): 880–83. http://dx.doi.org/10.7763/ijiet.2016.v6.809.

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2

BE, R. Aruna Sankari. "Cervical Cancer Detection Using Support Vector Machine." International journal of Emerging Trends in Science and Technology 04, no. 03 (2017): 5033–38. http://dx.doi.org/10.18535/ijetst/v4i3.08.

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3

Yousuf Wani, Aukif, and Preeti Sondhi. "Glaucoma Detection Using Support Vector Machine Algorithm." International Journal of Science and Research (IJSR) 10, no. 3 (2021): 376–79. https://doi.org/10.21275/sr21226092410.

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4

Huimin, Yao. "Research on Parallel Support Vector Machine Based on Spark Big Data Platform." Scientific Programming 2021 (December 17, 2021): 1–9. http://dx.doi.org/10.1155/2021/7998417.

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With the development of cloud computing and distributed cluster technology, the concept of big data has been expanded and extended in terms of capacity and value, and machine learning technology has also received unprecedented attention in recent years. Traditional machine learning algorithms cannot solve the problem of effective parallelization, so a parallelization support vector machine based on Spark big data platform is proposed. Firstly, the big data platform is designed with Lambda architecture, which is divided into three layers: Batch Layer, Serving Layer, and Speed Layer. Secondly, i

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5

Heo, Gyeong-Yong, and Seong-Hoon Kim. "Context-Aware Fusion with Support Vector Machine." Journal of the Korea Society of Computer and Information 19, no. 6 (2014): 19–26. http://dx.doi.org/10.9708/jksci.2014.19.6.019.

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6

Dong, Zengshou, Zhaojing Ren, and You Dong. "MECHANICAL FAULT RECOGNITION RESEARCH BASED ON LMD-LSSVM." Transactions of the Canadian Society for Mechanical Engineering 40, no. 4 (2016): 541–49. http://dx.doi.org/10.1139/tcsme-2016-0042.

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Mechanical fault vibration signals are non-stationary, which causes system instability. The traditional methods are difficult to accurately extract fault information and this paper proposes a local mean decomposition and least squares support vector machine fault identification method. The article introduces waveform matching to solve the original features of signals at the endpoints, using linear interpolation to get local mean and envelope function, then obtain production function PF vector through making use of the local mean decomposition. The energy entropy of PF vector take as identifica

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7

Guenther, Nick, and Matthias Schonlau. "Support Vector Machines." Stata Journal: Promoting communications on statistics and Stata 16, no. 4 (2016): 917–37. http://dx.doi.org/10.1177/1536867x1601600407.

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Support vector machines are statistical- and machine-learning techniques with the primary goal of prediction. They can be applied to continuous, binary, and categorical outcomes analogous to Gaussian, logistic, and multinomial regression. We introduce a new command for this purpose, svmachines. This package is a thin wrapper for the widely deployed libsvm (Chang and Lin, 2011, ACM Transactions on Intelligent Systems and Technology 2(3): Article 27). We illustrate svmachines with two examples.

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8

YANG, Zhi-Min, Yuan-Hai SHAO, and Jing LIANG. "Unascertained Support Vector Machine." Acta Automatica Sinica 39, no. 6 (2014): 895–901. http://dx.doi.org/10.3724/sp.j.1004.2013.00895.

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9

Zhang, L., W. Zhou, and L. Jiao. "Wavelet Support Vector Machine." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 34, no. 1 (2004): 34–39. http://dx.doi.org/10.1109/tsmcb.2003.811113.

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10

Navia-Vázquez, A., and E. Parrado-Hernández. "Support vector machine interpretation." Neurocomputing 69, no. 13-15 (2006): 1754–59. http://dx.doi.org/10.1016/j.neucom.2005.12.118.

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11

Reeves, D. M., and G. M. Jacyna. "Support vector machine regularization." Wiley Interdisciplinary Reviews: Computational Statistics 3, no. 3 (2011): 204–15. http://dx.doi.org/10.1002/wics.149.

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12

Lai, Lucas, and James Liu. "Support Vector Machine and Least Square Support Vector Machine Stock Forecasting Models." Computer Science and Information Technology 2, no. 1 (2014): 30–39. http://dx.doi.org/10.13189/csit.2014.020103.

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13

V., Dr Padmanabha Reddy. "Human Cognitive State classification using Support Vector Machine." Journal of Advanced Research in Dynamical and Control Systems 12, no. 01-Special Issue (2020): 46–54. http://dx.doi.org/10.5373/jardcs/v12sp1/20201045.

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14

Yousuf, Nazima, and Mrigana Walia. "Support Vector Machine Based MRI Brain Tumor Detection." International Journal of Science and Research (IJSR) 10, no. 2 (2021): 1682–86. https://doi.org/10.21275/sr21225214024.

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15

Jung, Kang-Mo. "Robust Algorithm for Multiclass Weighted Support Vector Machine." SIJ Transactions on Advances in Space Research & Earth Exploration 4, no. 3 (2016): 1–5. http://dx.doi.org/10.9756/sijasree/v4i3/0203430402.

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16

Dhaifallah, Mujahed Al, and K. S. Nisar. "Support Vector Machine Identification of Subspace Hammerstein Models." International Journal of Computer Theory and Engineering 7, no. 1 (2014): 9–15. http://dx.doi.org/10.7763/ijcte.2015.v7.922.

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17

Lee, Hee-Sung, Sung-Jun Hong, Byung-Yun Lee, and Eun-Tai Kim. "Design of Robust Support Vector Machine Using Genetic Algorithm." Journal of Korean Institute of Intelligent Systems 20, no. 3 (2010): 375–79. http://dx.doi.org/10.5391/jkiis.2010.20.3.375.

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18

Ovirianti, Nurul Huda, Muhammad Zarlis, and Herman Mawengkang. "Support Vector Machine Using A Classification Algorithm." SinkrOn 7, no. 3 (2022): 2103–7. http://dx.doi.org/10.33395/sinkron.v7i3.11597.

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Support vector machine is a part of machine learning approach based on statistical learning theory. Due to the higher accuracy of values, Support vector machines have become a focus for frequent machine learning users. This paper will introduce the basic theory of the Support vector machine, the basic idea of classification and the classification algorithm for the support vector machine that will be used. Solving the problem will use an algorithm, and prove the effectiveness of the algorithm on the data that has been used. In this study, the support vector machine has obtained very good accura

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19

Xu, Yuanfang. "Research on Automatic Recognition of New Words on Weibo." Advances in Education, Humanities and Social Science Research 7, no. 1 (2023): 653. http://dx.doi.org/10.56028/aehssr.7.1.653.2023.

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To effectively capture emerging vocabulary on Weibo, this article proposes a new Weibo new word recognition strategy that combines Weibo data and support vector machine. Firstly, select positive and negative example sentences from Weibo corpus and trained corpus with part of speech tagging. Then, the lexical features in these sentences are transformed into vectors, and then trained using support vector machines to obtain classification support vectors for Weibo new words. Finally, input the vectorized features into the already trained support vector machine classifier to identify new Weibo wor

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20

Shanmugapriya, P., and Y. Venkataramani. "Analysis of Speaker Verification System Using Support Vector Machine." JOURNAL OF ADVANCES IN CHEMISTRY 13, no. 10 (2017): 6531–42. http://dx.doi.org/10.24297/jac.v13i10.5839.

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The integration of GMM- super vector and Support Vector Machine (SVM) has become one of most popular strategy in text-independent speaker verification system.Â This paper describes the application of Fuzzy Support Vector Machine (FSVM) for classification of speakers using GMM-super vectors. Super vectors are formed by stacking the mean vectors of adapted GMMs from UBM using maximum a posteriori (MAP). GMM super vectors characterize speakerâ€™s acoustic characteristics which are used for developing a speaker dependent fuzzy SVM model. Introducing fuzzy theory in support vector machine yields be

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21

Jun, Sung-Hae. "Ubiquitous Data Mining Using Hybrid Support Vector Machine." Journal of Korean Institute of Intelligent Systems 15, no. 3 (2005): 312–17. http://dx.doi.org/10.5391/jkiis.2005.15.3.312.

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22

Hong, Euy-Seok. "Early Software Quality Prediction Using Support Vector Machine." Journal of the Korea society of IT services 10, no. 2 (2011): 235–45. http://dx.doi.org/10.9716/kits.2011.10.2.235.

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23

Nivethitha, T. Padma, A. Raynuka, and Dr J. G. R. Sathiaseelan. "Diagnosing Diabetes Using Support Vector Machine in Classification Techniques." International Journal of Trend in Scientific Research and Development Volume-2, Issue-5 (2018): 2208–14. http://dx.doi.org/10.31142/ijtsrd18251.

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24

Chen, Mubo, Binbin Fu, Taichun Tang, Jiali Ma, and Mingchui Dong. "Hierarchical Probabilistic Support Vector Machine for Detecting Cardiovascular Diseases." International Journal of Bioscience, Biochemistry and Bioinformatics 4, no. 5 (2014): 340–44. http://dx.doi.org/10.7763/ijbbb.2014.v4.367.

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25

Ts. YEO SIANG CHUAN, Ir. Dr. Lim Meng Hee, Dr. Hui Kar Hoou, and Eng Hoe Cheng. "Bayes' Theorem for Multi-Bearing Faults Diagnosis." International Journal of Automotive and Mechanical Engineering 20, no. 2 (2023): 10371–85. http://dx.doi.org/10.15282/ijame.20.2.2023.04.0802.

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During the process of fault diagnosis for automated machinery, support vector machines is one of the suitable choices to categorize multiple faults for machinery. Regardless of the volume of sampling data, support vector machines can handle a high number of input features. It was learned that support vector machines could only sense binary fault classification (such as faulty or healthy). However, the classification accuracy was found to be lower when using support vector machines to diagnose multi-bearing faults classifications. This is because the multiple classification problem will be redu

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26

Cuong, Nguyen The, and Huynh The Phung. "WEIGHTED STRUCTURAL SUPPORT VECTOR MACHINE." Journal of Computer Science and Cybernetics 37, no. 1 (2021): 43–56. http://dx.doi.org/10.15625/1813-9663/37/1/15396.

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In binary classification problems, two classes of data seem to be different from each other. It is expected to be more complicated due to the clusters in each class also tend to be different. Traditional algorithms as Support Vector Machine (SVM) or Twin Support Vector Machine (TWSVM) cannot sufficiently exploit structural information with cluster granularity of the data, cause limitation on the capability of simulation of data trends. Structural Twin Support Vector Machine (S-TWSVM) sufficiently exploits structural information with cluster granularity for learning a represented hyperplane. Th

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27

Chen, Haiyan, Ying Yu, Yizhen Jia, and Linghui Zhang. "Safe transductive support vector machine." Connection Science 34, no. 1 (2022): 942–59. http://dx.doi.org/10.1080/09540091.2021.2024511.

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28

Qiao, Xingye, and Lingsong Zhang. "Distance-weighted Support Vector Machine." Statistics and Its Interface 8, no. 3 (2015): 331–45. http://dx.doi.org/10.4310/sii.2015.v8.n3.a7.

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29

Kirinčić, Vedran, Ervin Čeperić, Saša Vlahinić, and Jonatan Lerga. "Support Vector Machine State Estimation." Applied Artificial Intelligence 33, no. 6 (2019): 517–30. http://dx.doi.org/10.1080/08839514.2019.1583452.

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30

Rodriguez-Lujan, Irene, Carlos Santa Cruz, and Ramon Huerta. "Hierarchical linear support vector machine." Pattern Recognition 45, no. 12 (2012): 4414–27. http://dx.doi.org/10.1016/j.patcog.2012.06.002.

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31

Kim, Hyun-Chul, Shaoning Pang, Hong-Mo Je, Daijin Kim, and Sung Yang Bang. "Constructing support vector machine ensemble." Pattern Recognition 36, no. 12 (2003): 2757–67. http://dx.doi.org/10.1016/s0031-3203(03)00175-4.

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32

Zhou, Shui-sheng, Hong-wei Liu, Li-hua Zhou, and Feng Ye. "Semismooth Newton support vector machine." Pattern Recognition Letters 28, no. 15 (2007): 2054–62. http://dx.doi.org/10.1016/j.patrec.2007.06.010.

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33

Cheng, Fanyong, Jing Zhang, Zuoyong Li, and Mingzhu Tang. "Double distribution support vector machine." Pattern Recognition Letters 88 (March 2017): 20–25. http://dx.doi.org/10.1016/j.patrec.2017.01.010.

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34

Jändel, Magnus. "A neural support vector machine." Neural Networks 23, no. 5 (2010): 607–13. http://dx.doi.org/10.1016/j.neunet.2010.01.002.

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35

Maali, Yashar, and Adel Al-Jumaily. "Self-advising support vector machine." Knowledge-Based Systems 52 (November 2013): 214–22. http://dx.doi.org/10.1016/j.knosys.2013.08.009.

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36

de Boves Harrington, Peter. "Support Vector Machine Classification Trees." Analytical Chemistry 87, no. 21 (2015): 11065–71. http://dx.doi.org/10.1021/acs.analchem.5b03113.

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37

Zhang, Li, and Wei-Da Zhou. "Fisher-regularized support vector machine." Information Sciences 343-344 (May 2016): 79–93. http://dx.doi.org/10.1016/j.ins.2016.01.053.

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38

Liu, Dalian, Yingjie Tian, Rongfang Bie, and Yong Shi. "Self-Universum support vector machine." Personal and Ubiquitous Computing 18, no. 8 (2014): 1813–19. http://dx.doi.org/10.1007/s00779-014-0797-9.

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39

Tian, YingJie, XuChan Ju, ZhiQuan Qi, and Yong Shi. "Improved twin support vector machine." Science China Mathematics 57, no. 2 (2013): 417–32. http://dx.doi.org/10.1007/s11425-013-4718-6.

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40

Xue, Hui, and Songcan Chen. "Glocalization pursuit support vector machine." Neural Computing and Applications 20, no. 7 (2010): 1043–53. http://dx.doi.org/10.1007/s00521-010-0448-7.

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41

Hwang, Jae Pil, Baehoon Choi, In Wha Hong, and Euntai Kim. "Multiclass Lagrangian support vector machine." Neural Computing and Applications 22, no. 3-4 (2011): 703–10. http://dx.doi.org/10.1007/s00521-011-0755-7.

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42

Kurita, Takio. "Support Vector Machine and Generalization." Journal of Advanced Computational Intelligence and Intelligent Informatics 8, no. 2 (2004): 84–92. http://dx.doi.org/10.20965/jaciii.2004.p0084.

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The support vector machine (SVM) has been extended to build up nonlinear classifiers using the kernel trick. As a learning model, it has the best recognition performance among the many methods currently known because it is devised to obtain high performance for unlearned data. This paper reviews how to enhance generalization in learning classifiers centering on the SVM.

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43

ZHANG, LI, WEI-DA ZHOU, TIAN-TIAN SU, and LI-CHENG JIAO. "DECISION TREE SUPPORT VECTOR MACHINE." International Journal on Artificial Intelligence Tools 16, no. 01 (2007): 1–15. http://dx.doi.org/10.1142/s0218213007003163.

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A new multi-class classifier, decision tree SVM (DTSVM) which is a binary decision tree with a very simple structure is presented in this paper. In DTSVM, a problem of multi-class classification is decomposed into a series of ones of binary classification. Here, the binary decision tree is generated by using kernel clustering algorithm, and each non-leaf node represents one binary classification problem. By compared with the other multi-class classification methods based on the binary classification SVMs, the scale and the complexity of DTSVM are less, smaller number of support vectors are nee

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44

Besrour, Amine, and Riadh Ksantini. "Incremental Subclass Support Vector Machine." International Journal on Artificial Intelligence Tools 28, no. 07 (2019): 1950020. http://dx.doi.org/10.1142/s0218213019500209.

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Support Vector Machine (SVM) is a very competitive linear classifier based on convex optimization problem, were support vectors fully describe decision boundary. Hence, SVM is sensitive to data spread and does not take into account the existence of class subclasses, nor minimizes data dispersion for classification performance improvement. Thus, Kernel subclass SVM (KSSVM) was proposed to handle multimodal data and to minimize data dispersion. Nevertheless, KSSVM has difficulties in classifying sequentially obtained data and handling large scale datasets, since it is based on batch learning. Fo

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45

González-Castaño, Francisco J., Ubaldo M. García-Palomares, and Robert R. Meyer. "Projection Support Vector Machine Generators." Machine Learning 54, no. 1 (2004): 33–44. http://dx.doi.org/10.1023/b:mach.0000008083.47006.86.

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46

Sabzekar, Mostafa, Hadi Sadoghi Yazdi, and Mahmoud Naghibzadeh. "Relaxed constraints support vector machine." Expert Systems 29, no. 5 (2011): 506–25. http://dx.doi.org/10.1111/j.1468-0394.2011.00611.x.

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47

Ding, Shifei, Fulin Wu, and Zhongzhi Shi. "Wavelet twin support vector machine." Neural Computing and Applications 25, no. 6 (2014): 1241–47. http://dx.doi.org/10.1007/s00521-014-1596-y.

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48

Sineglazov, Victor, and Andriy Samoshin. "Semi-supervised Support Vector Machine." Electronics and Control Systems 1, no. 75 (2023): 36–43. http://dx.doi.org/10.18372/1990-5548.75.17553.

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The article considers a new approach to constructing a support vector machine with semi-supervised learning for solving a classification problem. It is assumed that the distributions of the classes may overlap. The cost function has been modified by adding elements of a penalty to it for labels not in their class. The penalty is represented as a linear function of the distance between the label and the class boundary. To overcome the problem of multicriteria, a global optimization method known as continuation is proposed. For a combination of predictions, it is suggested to use the voting meth

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49

Reeves, D. M., and G. M. Jacyna. "Erratum: Support vector machine regularization." Wiley Interdisciplinary Reviews: Computational Statistics 3, no. 5 (2011): 481. http://dx.doi.org/10.1002/wics.188.

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50

Wang, Xuesong, Fei Huang, and Yuhu Cheng. "Computational performance optimization of support vector machine based on support vectors." Neurocomputing 211 (October 2016): 66–71. http://dx.doi.org/10.1016/j.neucom.2016.04.059.

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