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incremental training for SVM-Based Classification with Keyword Adjusting

Wuhan University Journal of Natural Sciences 2004年第5期9卷 805-811页

作者： SUNJin-wen YANGJian-wu LUBin XIAOJian-guo NationalKeyLaboratoryforTextProcessing.InstituteofComputerScienceandTechnology.PekingUniversity.Beijing100871.China

This paper analyzed the theory of incremental learning of SVM (support vector machine) and pointed out it is a shortage that the support vector optimization is only considered in present research of SVM incremental learning. According to the significance of keyword in training, a new incremental training method considering keyword adjusting was proposed, which eliminates the difference between incremental learning and batch learning through the keyword adjusting. The experimental results show that the improved method outperforms the method without the keyword adjusting and achieve the same precision as the batch method. Key words SVM (support vector machine) - incremental training - classification - keyword adjusting CLC number TP 18 Foundation item: Supported by the National Information Industry Development Foundation of ChinaBiography: SUN Jin-wen (1972-), male, Post-Doctoral, research direction: artificial intelligence, data mining and system integration.

关键词： SVM (support vector machine) incremental training classification keyword adjusting

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incremental training for SVM-Based Classification with Keyword Adjusting

Incremental Training for SVM-Based Classification with Keywo...

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The First Conference on Web Information System and Applications

作者： SUN Jin-wen, YANG Jian-wu, LU Bin, XIAO Jian-guo National Key Laboratory for Text Processing, Institute of Computer Science and Technology, Peking University, Beijing 100871, China

＜正＞ This paper analyzed the theory of incremental learning of SVM （support vector machine） and pointed out it is a shortage that the support vector optimization is only considered in present research of SVM incremental learning. According to the significance of keyword in training, a new incremental training method considering keyword adjusting was proposed, which eliminates the difference between incremental learning and batch learning through the keyword adjusting. The experimental results show that the improved method outperforms the method without the keyword adjusting and achieve the same precision as the batch method.

关键词： SVM(support vector machine) incremental training classification keyword adjusting

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A Fast Evolutionary Learning to Optimize CNN

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Chinese Journal of Electronics 2020年第6期29卷 1061-1073页

作者： CHEN Jinyin LIN Xiang GAO Shangtengda XIONG Hui ZHANG Longyuan LIU Yi XUAN Qi College of Information Engineering Zhejiang University of Technology NESA Lab Zhejiang University

Deep neural networks(DNNs) show great performance in lots of *** neural network(CNN) is one of the classic DNNs,and various modified CNNs have been brought up,such as DenseNet,GoogleNet,ResNet,*** diverse tasks,a unique structure of CNN may show its ***,how to design an effective CNN model for a practical task is a *** this paper,we model the architecture optimization of CNN as an optimization problem and design a Genetic network programming based Fast evolutionary learning(GNP-FEL) to optimize ***-FEL contains three main ideas:First GNP is adopted to optimize CNN architecture and hyperparameters,which can build diverse network structures and make network parameters selfevolve;Second multi-objective optimization is designed by balancing both CNN model efficiency and structure compactness;Last a novel incremental training method is proposed to train offspring CNN models in GNP,which is capable of reducing time complexity *** have validated that GNP-FEL can quickly evolve a CNN classifier with a sufficiently compact *** the classifier has a comparable classification effect to state-ofthe-art CNN model.

关键词： convolutional neural nets genetic algorithms learning (artificial intelligence) neural net architecture optimize CNN deep neural networks convolutional neural network DNN diverse tasks CNN model practical task architecture optimization optimization problem genetic network programming GNP-FEL diverse network structures network parameters multiobjective optimization structure compactness offspring CNN models CNN classifier state-ofthe-art CNN model fast evolutionary learning Evolutionary learning Genetic network programming (GNP) CNN Multi-objective optimization incremental training

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Novel Method of Mining Classification Information for SVM training

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Wuhan University Journal of Natural Sciences 2011年第6期16卷 475-480页

作者： SHEN Fengshan ZHANG Junying YUAN Xiguo School of Computer Science and Engineering Xidian UniversityXi＇an 710071 Shaanxi China

Support vector machine （SVM） is an important classi- fication tool in the pattern recognition and machine learning community, but its training is a time-consuming process. To deal with this problem, we propose a novel method to mine the useful information about classification hidden in the training sample for improving the training algorithm, and every training point is as- signed to a value that represents the classification information, respectively, where training points with the higher values are cho- sen as candidate support vectors for SVM training. The classifica- tion information value for a training point is computed based on the classification accuracy of an appropriate hyperplane for the training sample, where the hyperplane goes through the mapped target of the training point in feature space defined by a kernel fimction. Experimental results on various benchmark datasets show the effectiveness of our algorithm.

关键词： support vector machine (SVM) classification information incremental training candidate support vector

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