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Journal articles on the topic 'Incremental SVM'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Cao, Jian, Shi Yu Sun, and Xiu Sheng Duan. "Optimal Boundary SVM Incremental Learning Algorithm." Applied Mechanics and Materials 347-350 (August 2013): 2957–62. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.2957.

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Support vectors (SVs) cant be selected completely in support vector machine (SVM) incremental, resulting incremental learning process cant be sustained. In order to solve this problem, the article proposes optimal boundary SVM incremental learning algorithm. Based on in-depth analysis of the trend of the classification surface and make use of the KKT conditions, selecting the border of the vectors include the support vectors to participate SVM incremental learning. The experiment shows that the algorithm can be completely covered the support vectors and have the identical result with the class

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2

Gâlmeanu, Honorius, Lucian Mircea Sasu, and Razvan Andonie. "Incremental and Decremental SVM for Regression." International Journal of Computers Communications & Control 11, no. 6 (2016): 755. http://dx.doi.org/10.15837/ijccc.2016.6.2744.

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Training a support vector machine (SVM) for regression (function approximation) in an incremental/decremental way consists essentially in migrating the input vectors in and out of the support vector set with specific modification of the associated thresholds. We introduce with full details such a method, which allows for defining the exact increments or decrements associated with the thresholds before vector migrations take place. Two delicate issues are especially addressed: the variation of the regularization parameter (for tuning the model performance) and the extreme situations where the s

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He, Qing, Changying Du, Qun Wang, Fuzhen Zhuang, and Zhongzhi Shi. "A parallel incremental extreme SVM classifier." Neurocomputing 74, no. 16 (2011): 2532–40. http://dx.doi.org/10.1016/j.neucom.2010.11.036.

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4

Sun, Jie, Hui Li, Pei-Chann Chang, and Qing-Hua Huang. "Dynamic credit scoring using B & B with incremental-SVM-ensemble." Kybernetes 44, no. 4 (2015): 518–35. http://dx.doi.org/10.1108/k-02-2014-0036.

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Purpose – Previous researches on credit scoring mainly focussed on static modeling on panel sample data set in a certain period of time, and did not pay enough attention on dynamic incremental modeling. The purpose of this paper is to address the integration of branch and bound algorithm with incremental support vector machine (SVM) ensemble to make dynamic modeling of credit scoring. Design/methodology/approach – This new model hybridizes support vectors of old data with incremental financial data of corporate in the process of dynamic ensemble modeling based on bagged SVM. In the incremental

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Qin, Yuping, Dan Li, and Aihua Zhang. "A New SVM Multiclass Incremental Learning Algorithm." Mathematical Problems in Engineering 2015 (2015): 1–5. http://dx.doi.org/10.1155/2015/745815.

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A new support vector machine (SVM) multiclass incremental learning algorithm is proposed. To each class training sample, the hyperellipsoidal classifier that includes as many samples as possible and pushes the outlier samples away is trained in the feature space. When the new samples are added to the classification system, the algorithm reuses the old classifiers that have nothing to do with the new sample classes. To be classified sample, the Mahalanobis distances are used to decide the class of classified sample. If the sample point is not surrounded by any hyperellipsoidal or is surrounded

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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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Yao, Yibo, and Lawrence B. Holder. "Incremental SVM-based classification in dynamic streaming networks." Intelligent Data Analysis 20, no. 4 (2016): 825–52. http://dx.doi.org/10.3233/ida-160834.

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Ricci, Elisa, Luca Rugini, and Renzo Perfetti. "SVM-based CDMA receiver with incremental active learning." Neurocomputing 69, no. 13-15 (2006): 1691–96. http://dx.doi.org/10.1016/j.neucom.2006.01.015.

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9

Zhang, Shi Hua, Xi Long Qu, and Xue Ye Wang. "Incremental Regressive Learning Algorithm of Support Vector Machine and its Application." Advanced Materials Research 216 (March 2011): 301–6. http://dx.doi.org/10.4028/www.scientific.net/amr.216.301.

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There is no incremental learning ability for the traditional support vector machine (SVM) and there are all kind of merits and flaws for usually used incremental learning method. Normal SVM is unable to train in large-scale samples, while the computer’s memory is limited. In order to resolve this problem and improve training speed of the SVM, we analyze essential characteristic of SVM and bring up the incremental learning algorithm of SVM based on regression of SVM related to SV (support vectors). The algorithm increases the speed of training and can be able to learning with large-scale sample

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10

luying, Li, Zhou liling, and Tong xiaojun. "Research on Incremental Learning of SVM Based on Robustness." Journal of Physics: Conference Series 1060 (July 2018): 012053. http://dx.doi.org/10.1088/1742-6596/1060/1/012053.

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11

Jin-wen, Sun, Yang Jian-wu, Lu Bin, and Xiao Jian-guo. "Incremental training for SVM-based classification with keyword adjusting." Wuhan University Journal of Natural Sciences 9, no. 5 (2004): 805–11. http://dx.doi.org/10.1007/bf02831685.

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12

Wang, Jian Guo, Liang Wu Cheng, Wen Xing Zhang, and Bo Qin. "A Modified Incremental Support Vector Machine for Regression." Applied Mechanics and Materials 135-136 (October 2011): 63–69. http://dx.doi.org/10.4028/www.scientific.net/amm.135-136.63.

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support vector machine (SVM) has been shown to exhibit superior predictive power compared to traditional approaches in many studies, such as mechanical equipment monitoring and diagnosis. However, SVM training is very costly in terms of time and memory consumption due to the enormous amounts of training data and the quadratic programming problem. In order to improve SVM training speed and accuracy, we propose a modified incremental support vector machine (MISVM) for regression problems in this paper. The main concepts are that using the distance from the margin vectors which violate the Karush

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13

Yang Hai, Wei He, and Lei Fan. "An Incremental Learning Algorithm for SVM based on Voting Principle." International Journal of Information Processing and Management 2, no. 2 (2011): 8–14. http://dx.doi.org/10.4156/ijipm.vol2.issue2.2.

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14

García Molina, José Fernando, Lei Zheng, Metin Sertdemir, Dietmar J. Dinter, Stefan Schönberg, and Matthias Rädle. "Incremental Learning with SVM for Multimodal Classification of Prostatic Adenocarcinoma." PLoS ONE 9, no. 4 (2014): e93600. http://dx.doi.org/10.1371/journal.pone.0093600.

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15

Couellan, Nicolas P., and Theodore B. Trafalis. "On-line SVM learning via an incremental primal–dual technique." Optimization Methods and Software 28, no. 2 (2013): 256–75. http://dx.doi.org/10.1080/10556788.2011.633705.

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16

Yi, Yang, Jiansheng Wu, and Wei Xu. "Incremental SVM based on reserved set for network intrusion detection." Expert Systems with Applications 38, no. 6 (2011): 7698–707. http://dx.doi.org/10.1016/j.eswa.2010.12.141.

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17

Wang, Guang Bin, Xian Qiong Zhao, and Yu Hui He. "Fault Diagnosis Method Based on Supervised Incremental Local Tangent Space Alignment and SVM." Applied Mechanics and Materials 34-35 (October 2010): 1233–37. http://dx.doi.org/10.4028/www.scientific.net/amm.34-35.1233.

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To enhance the effect of fault diagnosis, a new fualt diagnosis method based on supervised incremental local tangent space alignment (SILTSA) and support vector machine (SVM) is proposed. The supervised learning approach is embedded into the incremental local tangent space alignment algorithm, to realize fault feature extraction and new data processing for equipment fault signal, and then correctly classify the faults by non-linear support vector machines. The experiment result for roller bearing fault diagnosis shows that SILTSA-SVM method has better diagnosis effect to related methods

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18

Sun, Zhen Long, Ai Long Fan, and Da Lu Guan. "Power System Transient Stability Based on SVM." Advanced Materials Research 562-564 (August 2012): 1476–78. http://dx.doi.org/10.4028/www.scientific.net/amr.562-564.1476.

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In order to overcome the lack of which power system transient stability assessment model can not continue to learn and update the model online, in this chapter, a incremental learning method of support vector machine is proposed . The new data is added to the solution by constructing a recursive solution , which provides a new way of learning online for power system transient stability assessment.

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19

Luo, Jian, Jin Tang, and Xiaoming Xiao. "Abnormal Gait Behavior Detection for Elderly Based on Enhanced Wigner-Ville Analysis and Cloud Incremental SVM Learning." Journal of Sensors 2016 (2016): 1–18. http://dx.doi.org/10.1155/2016/5869238.

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A cloud based health care system is proposed in this paper for the elderly by providing abnormal gait behavior detection, classification, online diagnosis, and remote aid service. Intelligent mobile terminals with triaxial acceleration sensor embedded are used to capture the movement and ambulation information of elderly. The collected signals are first enhanced by a Kalman filter. And the magnitude of signal vector features is then extracted and decomposed into a linear combination of enhanced Gabor atoms. The Wigner-Ville analysis method is introduced and the problem is studied by joint time

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20

Soula, Arbia, Khaoula Tbarki, Riadh Ksantini, Salma Ben Said, and Zied Lachiri. "A novel incremental Kernel Nonparametric SVM model (iKN-SVM) for data classification: An application to face detection." Engineering Applications of Artificial Intelligence 89 (March 2020): 103468. http://dx.doi.org/10.1016/j.engappai.2019.103468.

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21

Tang, Ju, Ran Zhuo, DiBo Wang, JianRong Wu, and XiaoXing Zhang. "Application of SA-SVM Incremental Algorithm in GIS PD Pattern Recognition." Journal of Electrical Engineering and Technology 11, no. 1 (2016): 192–99. http://dx.doi.org/10.5370/jeet.2016.11.1.192.

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22

Dagher, Issam, and Fady Azar. "Improving the SVM gender classification accuracy using clustering and incremental learning." Expert Systems 36, no. 3 (2019): e12372. http://dx.doi.org/10.1111/exsy.12372.

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23

Li, Jingmei, Di Xue, Weifei Wu, and Jiaxiang Wang. "Incremental Learning for Malware Classification in Small Datasets." Security and Communication Networks 2020 (February 20, 2020): 1–12. http://dx.doi.org/10.1155/2020/6309243.

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Information security is an important research area. As a very special yet important case, malware classification plays an important role in information security. In the real world, the malware datasets are open-ended and dynamic, and new malware samples belonging to old classes and new classes are increasing continuously. This requires the malware classification method to enable incremental learning, which can efficiently learn the new knowledge. However, existing works mainly focus on feature engineering with machine learning as a tool. To solve the problem, we present an incremental malware

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24

PENG, BINBIN, WENYIN LIU, YIN LIU, GUANGLIN HUANG, ZHENGXING SUN, and XIANGYU JIN. "AN SVM-BASED INCREMENTAL LEARNING ALGORITHM FOR USER ADAPTATION OF SKETCH RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 18, no. 08 (2004): 1529–50. http://dx.doi.org/10.1142/s0218001404003769.

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User adaptation is a critical problem in the design of human-computer interaction systems. Many pattern recognition problems, such as handwriting/sketching recognition and speech recognition, are user dependent, since different users' handwritings, drawing styles, and accents are different. Therefore, the classifiers for these problems should provide the functionality of user adaptation so as to let each particular user experience better recognition accuracy according to his input habit/style. However, the user adaptation functionality requires the classifiers to have the incremental learning

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25

Couellan, Nicolas, and Sophie Jan. "Incremental accelerated gradient methods for SVM classification: study of the constrained approach." Computational Management Science 11, no. 4 (2013): 419–44. http://dx.doi.org/10.1007/s10287-013-0186-2.

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26

Chitrakar, Roshan, and Chuanhe Huang. "Selection of Candidate Support Vectors in incremental SVM for network intrusion detection." Computers & Security 45 (September 2014): 231–41. http://dx.doi.org/10.1016/j.cose.2014.06.006.

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27

Li, Jinfeng, and Wenhao Xie. "Research of Incremental Learning Algorithm for SVM Based on Class Center Diameter." Journal of Physics: Conference Series 1894, no. 1 (2021): 012074. http://dx.doi.org/10.1088/1742-6596/1894/1/012074.

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28

Thakur, Rashmi K., and Manojkumar V. Deshpande. "OKO-SVM: Online kernel optimization-based support vector machine for the incremental learning and classification of the sentiments in the train reviews." International Journal of Modeling, Simulation, and Scientific Computing 09, no. 06 (2018): 1850054. http://dx.doi.org/10.1142/s179396231850054x.

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Online incremental learning is one of the emerging research interests among the researchers in the recent years. The sentiment classification through the online incremental learning faces many challenges due to the limitations in the memory and the computing resources available for processing the online reviews. This work has introduced an online incremental learning algorithm for classifying the train reviews. The sentiments available in the reviews provided for the public services are necessary for improving the quality of the service. This work proposes the online kernel optimization-based

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29

Wang, Guang Bin, Xue Jun Li, Zhi Cheng He, and Y. Q. Kong. "Fault Diagnosis Method Based on Supervised Manifold Learning and SVM." Advanced Materials Research 216 (March 2011): 223–27. http://dx.doi.org/10.4028/www.scientific.net/amr.216.223.

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In order to better identify the fault of bearing,one new fualt diagnosis method based on supervised Linear local tangent space alignment (SLLTSA) and support vector machine (SVM) is proposed..In this methd, the supervised learning is embedded into the linear local tangent space alignment algorithm,making full use of experience category information for fault feature extraction, and then using linear transformation matrix to fast process the new monitoring data, finally distinguishing fault status of the incremental data by nonlinear SVM algorithm. The experiment result for roller bearing fault

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30

Fu, Ke Chang, Zhu Ming, Peng Liu, and Guo Jiang Wang. "Incremental Fault Diagnosis for Nonlinear Processes." Advanced Materials Research 433-440 (January 2012): 6430–36. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.6430.

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A new faults classification method based on on-line independent support vector machine (OISVM) is proposed for fault diagnosis in nonlinear processes. Fault diagnosis can be taken as a pattern recognition problem. As most processes are intrinsically nonlinear, support vector machines (SVMs) are one of the most popular and promising classification algorithms. The fatal drawbacks of standard SVM is the computing overhead grows with the number of training samples, where as training samples from real industrial processes are increasing with time grows. An incremental fault diagnosis approach based

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31

Zhenyu Wu, Jun Sun, Lin Feng, and Bo Jin. "A Policy of Cluster Analyzing Applied to Incremental SVM Learning with Temporal Information." Journal of Convergence Information Technology 6, no. 7 (2011): 194–202. http://dx.doi.org/10.4156/jcit.vol6.issue7.24.

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32

Yin, Yingjie, De Xu, Xingang Wang, and Mingran Bai. "Online State-Based Structured SVM Combined With Incremental PCA for Robust Visual Tracking." IEEE Transactions on Cybernetics 45, no. 9 (2015): 1988–2000. http://dx.doi.org/10.1109/tcyb.2014.2363078.

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33

Wu, Chunfang, Ruisheng Jia, and Tao Qiu. "Rock Burst Monitoring and Early Warning Based on Incremental Learning Method with SVM." Research Journal of Information Technology 5, no. 4 (2013): 121–24. http://dx.doi.org/10.19026/rjit.5.5797.

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34

Doan, Thanh-Nghi, Thanh-Nghi Do, and François Poulet. "Parallel incremental power mean SVM for the classification of large-scale image datasets." International Journal of Multimedia Information Retrieval 3, no. 2 (2014): 89–96. http://dx.doi.org/10.1007/s13735-014-0053-0.

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35

Qin, Liang, Hong Wei Yin, Xian Jun Shi, and Zhi Cai Xiao. "An Improved Incremental Training Algorithm of Support Vector Machines." Advanced Materials Research 301-303 (July 2011): 677–81. http://dx.doi.org/10.4028/www.scientific.net/amr.301-303.677.

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In order to figure out the deficiency of the SVM on extensive sample, nature of SV is studied in this paper. An improved incremental training algorithm is put forward based on dimensional of samples. A chosen gene which got by density and distance criterion is used in this method. In this method the number of training samples is decreased and the space information is keeped. So, the training speed is improved while the precision is not reduced. And the simulation proved the efficiency of this method.

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36

Sun, Zong Hai. "Constraint Projection Adaptive Natural Gradient Online Algorithm for SVM." Advanced Materials Research 139-141 (October 2010): 1692–96. http://dx.doi.org/10.4028/www.scientific.net/amr.139-141.1692.

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The training of Support Vector Machine (SVM) is an optimization problem of quadratic programming which can not be applied to the online training in real time applications or time-variant data source. The online algorithms proposed by other researchers have high computational complexity and slow training speed, which can not be well applied to the time-variant problems as well. In this paper the projection gradient and adaptive natural gradient is combined. The constraint projection adaptive natural gradient online algorithm for SVM is proposed. The computation complexity of the constraint proj

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Bo Wen, Ganlin Shan, and Xiusheng Duan. "Progressive Incremental Learning Algorithm of the Support Vector Machine SVM based on Cross Validation." International Journal of Advancements in Computing Technology 5, no. 7 (2013): 621–27. http://dx.doi.org/10.4156/ijact.vol5.issue7.76.

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38

Xie, Wenhao, Gongqian Liang, and Pengcheng Yuan. "Research on the incremental learning SVM algorithm based on the improved generalized KKT condition." Journal of Physics: Conference Series 1237 (June 2019): 022150. http://dx.doi.org/10.1088/1742-6596/1237/2/022150.

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39

XU, XIN, and WEI WANG. "AN INCREMENTAL GRAY RELATIONAL ANALYSIS ALGORITHM FOR MULTI-CLASS CLASSIFICATION AND OUTLIER DETECTION." International Journal of Pattern Recognition and Artificial Intelligence 26, no. 06 (2012): 1250011. http://dx.doi.org/10.1142/s0218001412500115.

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The incremental classifier is superior in saving significant computational cost by incremental learning on continuously increasing training data. However, existing classification algorithms are problematic when applied for incremental learning for multi-class classification. First, some algorithms, such as neural network and SVM, are not inexpensive for incremental learning due to their complex architectures. When applied for multi-class classification, the computational cost would rise dramatically when the class number increases. Second, existing incremental classification algorithms are usu

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Kim, Junmo, Geunbo Yang, Juhyeong Kim, Seungmin Lee, Ko Keun Kim, and Cheolsoo Park. "Efficiently Updating ECG-Based Biometric Authentication Based on Incremental Learning." Sensors 21, no. 5 (2021): 1568. http://dx.doi.org/10.3390/s21051568.

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Recently, the interest in biometric authentication based on electrocardiograms (ECGs) has increased. Nevertheless, the ECG signal of a person may vary according to factors such as the emotional or physical state, thus hindering authentication. We propose an adaptive ECG-based authentication method that performs incremental learning to identify ECG signals from a subject under a variety of measurement conditions. An incremental support vector machine (SVM) is adopted for authentication implementing incremental learning. We collected ECG signals from 11 subjects during 10 min over six days and u

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41

Cai, Jun, and Rui Liu. "An Improved Online SVM Algorithm for Overcoming the Influence of Muscle Fatigue in sEMG Based Human Machine Interaction." Applied Mechanics and Materials 536-537 (April 2014): 1026–31. http://dx.doi.org/10.4028/www.scientific.net/amm.536-537.1026.

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For the problem that the stability of surface Electromyographic EMG(sEMG) based human-machine interface(HMI) declines as the muscle fatigue takes place, an improved incremental training algorithm for online support vector machine(SVM) is proposed. This paper study the changes of sEMG when muscle fatigue occurs by the method of continuous wavelet transform, and then apply the improved online SVM for sEMG classification. The novel method adjusts the parameters of SVM model to adapt itself based on the changes of sEMG signals and the training data is conditionally selected and forgot. Experiment

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42

Chen, Jiusheng, Xingkai Xu, and Xiaoyu Zhang. "Fault Detection for Turbine Engine Disk Based on Adaptive Weighted One-Class Support Vector Machine." Journal of Electrical and Computer Engineering 2020 (August 28, 2020): 1–10. http://dx.doi.org/10.1155/2020/9898546.

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Fault detection for turbine engine components is becoming increasingly important for the efficient running of commercial aircraft. Recently, the support vector machine (SVM) with kernel function is the most popular technique for monitoring nonlinear processes, which can better handle the nonlinear representation of fault detection of turbine engine disk. In this paper, an adaptive weighted one-class SVM-based fault detection method coupled with incremental and decremental strategy is proposed, which can efficiently solve the time series data stream drifting problem. To update the efficient tra

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43

GAO Yuan. "Research and Simulation on The Fast Incremental SVM Intrusion Detection Algorithm Based on Adjacent Boundary Area." International Journal of Digital Content Technology and its Applications 6, no. 22 (2012): 262–69. http://dx.doi.org/10.4156/jdcta.vol6.issue22.29.

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Cheng, Wei-Yuan, and Chia-Feng Juang. "A Fuzzy Model With Online Incremental SVM and Margin-Selective Gradient Descent Learning for Classification Problems." IEEE Transactions on Fuzzy Systems 22, no. 2 (2014): 324–37. http://dx.doi.org/10.1109/tfuzz.2013.2254492.

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Pronobis, Andrzej, Luo Jie, and Barbara Caputo. "The more you learn, the less you store: Memory-controlled incremental SVM for visual place recognition." Image and Vision Computing 28, no. 7 (2010): 1080–97. http://dx.doi.org/10.1016/j.imavis.2010.01.015.

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Xiang, Ruxi, Xifang Zhu, Feng Wu, Qinquan Xu, and Jianwei Li. "Object Tracking Based on Online Semi-Supervised SVM and Adaptive-Fused Feature." Cybernetics and Information Technologies 16, no. 2 (2016): 198–211. http://dx.doi.org/10.1515/cait-2016-0030.

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Abstract In order to improve the performance of tracking, we propose a new online tracking method based on classification and adaptive fused feature. We first label a few positive and negative samples, train the classifier by the online SSSM (Semi-Supervised Support Vector Machine) learning and these labelled samples, and then locate the position of the object from the next frame according to the trained classifier. In order to adapt more of the new samples, we need to update the classifier by finding new samples with high confident value obtained by the trained classifier and add them into th

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47

Nivre, Joakim. "Algorithms for Deterministic Incremental Dependency Parsing." Computational Linguistics 34, no. 4 (2008): 513–53. http://dx.doi.org/10.1162/coli.07-056-r1-07-027.

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Parsing algorithms that process the input from left to right and construct a single derivation have often been considered inadequate for natural language parsing because of the massive ambiguity typically found in natural language grammars. Nevertheless, it has been shown that such algorithms, combined with treebank-induced classifiers, can be used to build highly accurate disambiguating parsers, in particular for dependency-based syntactic representations. In this article, we first present a general framework for describing and analyzing algorithms for deterministic incremental dependency par

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Fan, Jin, Xian Kun Zhang, Xue Tian, and Dong Liu. "An Adaptive Topic Crawler for Electronic Public Opinion." Advanced Materials Research 765-767 (September 2013): 1451–55. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.1451.

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Topic crawler is a tool for collecting electronic public opinion from the internet. The identification method of topics relevance identification directly affects the acquisition rate of topic crawler. To improve the low information acquisition rate of existing topic crawlers strategy, a modified SVM classifier algorithm which is based on online incremental learning is proposed. The idea of algorithm is to remove samples that affect the training set greatly in the historical training set, and then to re-train the historical set and the incremental set to obtain a complete training set. A framew

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Qin, Jianzhao, Yuanqing Li, and Wei Sun. "A Semisupervised Support Vector Machines Algorithm for BCI Systems." Computational Intelligence and Neuroscience 2007 (2007): 1–9. http://dx.doi.org/10.1155/2007/94397.

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As an emerging technology, brain-computer interfaces (BCIs) bring us new communication interfaces which translate brain activities into control signals for devices like computers, robots, and so forth. In this study, we propose a semisupervised support vector machine (SVM) algorithm for brain-computer interface (BCI) systems, aiming at reducing the time-consuming training process. In this algorithm, we apply a semisupervised SVM for translating the features extracted from the electrical recordings of brain into control signals. This SVM classifier is built from a small labeled data set and a l

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Chen, Bin, and Bo Meng. "Power Transformer Fault Diagnosis System Based on Learn++." Applied Mechanics and Materials 602-605 (August 2014): 2053–56. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.2053.

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Aiming at the shortages of traditional method for power transformer fault diagnosis, the ensemble idea and incremental learning idea are used for better performance. The SVM is selected to establish the fault diagnosis models as sub learning machines. And then, the Learn++ algorithm is used to aggregate the sub learning machines. The new with new method will ensure the accuracy of fault diagnosis, and will update online. The experiments demonstrate that the performance of power transformer fault diagnosis system based on Learn++ is the best.

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