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Journal articles on the topic 'Support Vector Machine'

To see the other types of publications on this topic, follow the link: Support Vector Machine.

Author: Grafiati

Published: 4 June 2021

Last updated: 1 June 2024

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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 (March 31, 2017): 5033–38. http://dx.doi.org/10.18535/ijetst/v4i3.08.

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3

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 (June 30, 2014): 19–26. http://dx.doi.org/10.9708/jksci.2014.19.6.019.

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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, in order to improve the training efficiency of support vector machines on large-scale data, when merging two support vector machines, the “special points” other than support vectors are considered, that is, the points where the nonsupport vectors in one subset violate the training results of the other subset, and a cross-validation merging algorithm is proposed. Then, a parallelized support vector machine based on cross-validation is proposed, and the parallelization process of the support vector machine is realized on the Spark platform. Finally, experiments on different datasets verify the effectiveness and stability of the proposed method. Experimental results show that the proposed parallelized support vector machine has outstanding performance in speed-up ratio, training time, and prediction accuracy.

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5

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 (February 13, 2020): 46–54. http://dx.doi.org/10.5373/jardcs/v12sp1/20201045.

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6

Jung, Kang-Mo. "Robust Algorithm for Multiclass Weighted Support Vector Machine." SIJ Transactions on Advances in Space Research & Earth Exploration 4, no. 3 (June 10, 2016): 1–5. http://dx.doi.org/10.9756/sijasree/v4i3/0203430402.

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7

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 (February 2014): 9–15. http://dx.doi.org/10.7763/ijcte.2015.v7.922.

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8

YANG, Zhi-Min, Yuan-Hai SHAO, and Jing LIANG. "Unascertained Support Vector Machine." Acta Automatica Sinica 39, no. 6 (March 25, 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 (February 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 (August 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 (March 8, 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 (January 2014): 30–39. http://dx.doi.org/10.13189/csit.2014.020103.

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13

Guenther, Nick, and Matthias Schonlau. "Support Vector Machines." Stata Journal: Promoting communications on statistics and Stata 16, no. 4 (December 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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14

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 (June 25, 2010): 375–79. http://dx.doi.org/10.5391/jkiis.2010.20.3.375.

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15

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 (November 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 identification input vectors. These vectors are respectively inputted BP neural networks, support vector machines, least squares support vector machines to identify faults. Experimental result show that the accuracy of least squares support vector machine with higher classification accuracy has been improved.

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16

Jun, Sung-Hae. "Ubiquitous Data Mining Using Hybrid Support Vector Machine." Journal of Korean Institute of Intelligent Systems 15, no. 3 (June 1, 2005): 312–17. http://dx.doi.org/10.5391/jkiis.2005.15.3.312.

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17

Hong, Euy-Seok. "Early Software Quality Prediction Using Support Vector Machine." Journal of the Korea society of IT services 10, no. 2 (June 30, 2011): 235–45. http://dx.doi.org/10.9716/kits.2011.10.2.235.

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18

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 (August 31, 2018): 2208–14. http://dx.doi.org/10.31142/ijtsrd18251.

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19

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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20

Ovirianti, Nurul Huda, Muhammad Zarlis, and Herman Mawengkang. "Support Vector Machine Using A Classification Algorithm." SinkrOn 7, no. 3 (August 13, 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 accuracy results in its completion. The SVM classification uses kernel RBF with optimum parameters Cost = 5 and gamma = 2 is 88%.

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21

Shanmugapriya, P., and Y. Venkataramani. "Analysis of Speaker Verification System Using Support Vector Machine." JOURNAL OF ADVANCES IN CHEMISTRY 13, no. 10 (February 25, 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 better classification accuracy and requires less number of support vectors. Experiments were conducted on 2001 NIST speaker recognition evaluation corpus. Performance of GMM-FSVM based speaker verification system is compared with the conventional GMM-UBM and GMM-SVM based systems.Â Experimental results indicate that the fuzzy SVM based speaker verification system with GMM super vector achieves better performance to GMM-UBM system. Â

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22

Xu, Yuanfang. "Research on Automatic Recognition of New Words on Weibo." Advances in Education, Humanities and Social Science Research 7, no. 1 (October 10, 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 words. Based on the experimental results, the system found the optimal feature combination.

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23

Cuong, Nguyen The, and Huynh The Phung. "WEIGHTED STRUCTURAL SUPPORT VECTOR MACHINE." Journal of Computer Science and Cybernetics 37, no. 1 (March 29, 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. Therefore, the capability of S-TWSVM’s data simulation is better than that of TWSVM. However, for the datasets where each class consists of clusters of different trends, the S-TWSVM’s data simulation capability seems restricted. Besides, the training time of S-TWSVM has not been improved compared to TWSVM. This paper proposes a new Weighted Structural - Support Vector Machine (called WS-SVM) for binary classification problems with a class-vs-clusters strategy. Experimental results show that WS-SVM could describe the tendency of the distribution of cluster information. Furthermore, both the theory and experiment show that the training time of the WS-SVM for classification problem has significantly improved compared to S-TWSVM.

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24

Chen, Haiyan, Ying Yu, Yizhen Jia, and Linghui Zhang. "Safe transductive support vector machine." Connection Science 34, no. 1 (February 7, 2022): 942–59. http://dx.doi.org/10.1080/09540091.2021.2024511.

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25

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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26

Kirinčić, Vedran, Ervin Čeperić, Saša Vlahinić, and Jonatan Lerga. "Support Vector Machine State Estimation." Applied Artificial Intelligence 33, no. 6 (March 5, 2019): 517–30. http://dx.doi.org/10.1080/08839514.2019.1583452.

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27

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

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28

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

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29

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

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30

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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31

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

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32

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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33

de Boves Harrington, Peter. "Support Vector Machine Classification Trees." Analytical Chemistry 87, no. 21 (October 22, 2015): 11065–71. http://dx.doi.org/10.1021/acs.analchem.5b03113.

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34

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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35

Liu, Dalian, Yingjie Tian, Rongfang Bie, and Yong Shi. "Self-Universum support vector machine." Personal and Ubiquitous Computing 18, no. 8 (August 31, 2014): 1813–19. http://dx.doi.org/10.1007/s00779-014-0797-9.

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36

Tian, YingJie, XuChan Ju, ZhiQuan Qi, and Yong Shi. "Improved twin support vector machine." Science China Mathematics 57, no. 2 (December 14, 2013): 417–32. http://dx.doi.org/10.1007/s11425-013-4718-6.

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37

Xue, Hui, and Songcan Chen. "Glocalization pursuit support vector machine." Neural Computing and Applications 20, no. 7 (September 23, 2010): 1043–53. http://dx.doi.org/10.1007/s00521-010-0448-7.

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38

Hwang, Jae Pil, Baehoon Choi, In Wha Hong, and Euntai Kim. "Multiclass Lagrangian support vector machine." Neural Computing and Applications 22, no. 3-4 (December 8, 2011): 703–10. http://dx.doi.org/10.1007/s00521-011-0755-7.

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39

Kurita, Takio. "Support Vector Machine and Generalization." Journal of Advanced Computational Intelligence and Intelligent Informatics 8, no. 2 (March 20, 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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40

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 (February 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 needed, and has faster test speed. The final simulation results confirm the feasibility and the validity of DTSVM.

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41

Besrour, Amine, and Riadh Ksantini. "Incremental Subclass Support Vector Machine." International Journal on Artificial Intelligence Tools 28, no. 07 (November 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. For this reason, we propose a novel incremental KSSVM (iKSSVM) which handles dynamic and large data in a proper manner. The iKSSVM is still based on convex optimization problem and minimizes data dispersion within and between data subclasses incrementally, in order to improve discriminative power and classification performance. An extensive comparative evaluation of the iKSSVM to batch KSSVM, as well as, other contemporary incremental classifiers, on real world datasets, has shown clearly its superiority in terms of classification accuracy.

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42

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

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43

Sabzekar, Mostafa, Hadi Sadoghi Yazdi, and Mahmoud Naghibzadeh. "Relaxed constraints support vector machine." Expert Systems 29, no. 5 (September 2, 2011): 506–25. http://dx.doi.org/10.1111/j.1468-0394.2011.00611.x.

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44

Ding, Shifei, Fulin Wu, and Zhongzhi Shi. "Wavelet twin support vector machine." Neural Computing and Applications 25, no. 6 (April 23, 2014): 1241–47. http://dx.doi.org/10.1007/s00521-014-1596-y.

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45

Sineglazov, Victor, and Andriy Samoshin. "Semi-supervised Support Vector Machine." Electronics and Control Systems 1, no. 75 (March 26, 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 method of models with different kernels. The Optuna framework was chosen as the tool for configuring hyperparameters. The following were considered as training samples: type_dataset, banana, banana_inverse, c_circles, two_moons_classic, two_moons_tight, two_moons_wide.

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46

Reeves, D. M., and G. M. Jacyna. "Erratum: Support vector machine regularization." Wiley Interdisciplinary Reviews: Computational Statistics 3, no. 5 (August 2, 2011): 481. http://dx.doi.org/10.1002/wics.188.

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47

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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48

Seok, Kyungha, and Daehyun Cho. "A Study on Support Vectors of Least Squares Support Vector Machine." Communications for Statistical Applications and Methods 10, no. 3 (December 1, 2003): 873–78. http://dx.doi.org/10.5351/ckss.2003.10.3.873.

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49

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 (June 30, 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 reduced into several sub-problems of binary classification when support vector machines adapt to multi-bearing faults classifications. From there, many contradictory results will occur from every support vector machine model. In order to solve the situation, the combination of Support Vector Machines and Bayes’ Theorem is introduced to every single support vector machine model to overcome the conflicting results. This method will also increase classification accuracy. The proposed Support Vector Machines - Bayes’ Theorem method has resulted in an increase in the accuracy of the fault diagnosis model. The analysis result has shown an accuracy from 72% to 95%. It proved that Support Vector Machines - Bayes’ Theorem continuously eliminates and refines conflicting results from the original support vector machine model. Compared to the existing support vector machine, the proposed Support Vector Machines - Bayes’ Theorem has proven its effectiveness in diagnosing the multi-bearing faults problem classification.

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50

Seo. "Development of Audio Watermark Decoding Model Using Support Vector Machine." Journal of the Acoustical Society of Korea 33, no. 6 (2014): 400. http://dx.doi.org/10.7776/ask.2014.33.6.400.

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