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80 results on '"Liang, Xun"'

1. A Multiple Kernel Learning Model Based on p -Norm.

Author

Qi J, Liang X, and Xu R

Subjects
Algorithms, Artificial Intelligence, Pattern Recognition, Automated, Support Vector Machine
Abstract

By utilizing kernel functions, support vector machines (SVMs) successfully solve the linearly inseparable problems. Subsequently, its applicable areas have been greatly extended. Using multiple kernels (MKs) to improve the SVM classification accuracy has been a hot topic in the SVM research society for several years. However, most MK learning (MKL) methods employ L 1 -norm constraint on the kernel combination weights, which forms a sparse yet nonsmooth solution for the kernel weights. Alternatively, the L p -norm constraint on the kernel weights keeps all information in the base kernels. Nonetheless, the solution of L p -norm constraint MKL is nonsparse and sensitive to the noise. Recently, some scholars presented an efficient sparse generalized MKL ( L 1 - and L 2 -norms based GMKL) method, in which L 1    L 2 established an elastic constraint on the kernel weights. In this paper, we further extend the GMKL to a more generalized MKL method based on the p -norm, by joining L 1 - and L p -norms. Consequently, the L 1 - and L 2 -norms based GMKL is a special case in our method when p = 2. Experiments demonstrated that our L 1 - and L p -norms based MKL offers a higher accuracy than the L 1 - and L 2 -norms based GMKL in the classification, while keeping the properties of the L 1 - and L 2 -norms based on GMKL.

Published
2018
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2. Hyperellipsoidal statistical classifications in a reproducing kernel Hilbert space.

Author

Liang X and Ni Z

Subjects
Computer Simulation, Algorithms, Artificial Intelligence, Models, Statistical, Pattern Recognition, Automated methods
Abstract

Standard support vector machines (SVMs) have kernels based on the Euclidean distance. This brief extends standard SVMs to SVMs with kernels based on the Mahalanobis distance. The extended SVMs become a special case of the Euclidean distance when the covariance matrix in a reproducing kernel Hilbert space is degenerated to an identity. The Mahalanobis distance leads to hyperellipsoidal kernels and the Euclidean distance results in hyperspherical ones. In this brief, the Mahalanobis distance-based kernel in a reproducing kernel Hilbert space is developed systematically. Extensive experiments demonstrate that the hyperellipsoidal kernels slightly outperform the hyperspherical ones, with fewer SVs.

Published
2011
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3. An effective method of pruning support vector machine classifiers.

Author

Liang X

Subjects
Computer Simulation, Humans, Models, Statistical, Software, Algorithms, Artificial Intelligence, Numerical Analysis, Computer-Assisted
Abstract

Support vector machine (SVM) classifiers often contain many SVs, which lead to high computational cost at runtime and potential overfitting. In this paper, a practical and effective method of pruning SVM classifiers is systematically developed. The kernel row vectors, with one-to-one correspondence to the SVs, are first organized into clusters. The pruning work is divided into two phases. In the first phase, orthogonal projections (OPs) are performed to find kernel row vectors that can be approximated by the others. In the second phase, the previously found vectors are removed, and crosswise propagations, which simply utilize the coefficients of OPs, are implemented within each cluster. The method circumvents the problem of explicitly discerning SVs in the high-dimensional feature space as the SVM formulation does, and does not involve local minima. With different parameters, 3000 experiments were run on the LibSVM software platform. After pruning 42% of the SVs, the average change in classification accuracy was only - 0.7%, and the average computation time for removing one SV was 0.006 of the training time. In some scenarios, over 90% of the SVs were pruned with less than 0.1% reduction in classification accuracy. The experiments demonstrate the existence of large numbers of superabundant SVs in trained SVMs, and suggest a synergistic use of training and pruning in practice. Many SVMs already used in applications could be upgraded by pruning nearly half of their SVs.

Published
2010
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4. Pruning support vector machines without altering performances.

Author

Liang X, Chen RC, and Guo X

Subjects
Computer Simulation, Algorithms, Artificial Intelligence, Models, Theoretical, Numerical Analysis, Computer-Assisted
Abstract

Support vector machines (SV machines, SVMs) have many merits that distinguish themselves from many other machine-learning algorithms, such as the nonexistence of local minima, the possession of the largest distance from the separating hyperplane to the SVs, and a solid theoretical foundation. However, SVM training algorithms such as the efficient sequential minimal optimization (SMO) often produce many SVs. Some scholars have found that the kernel outputs are frequently of similar levels, which insinuate the redundancy of SVs. By analyzing the overlapped information of kernel outputs, a succinct separating-hyperplane-securing method for pruning the dispensable SVs based on crosswise propagation (CP) is systematically developed. The method also circumvents the problem of explicitly discerning SVs in feature space as the SVM formulation does. Experiments with the famous SMO-based software LibSVM reveal that all typical kernels with different parameters on the data sets contribute the dispensable SVs. Some 1% ~ 9% (in some scenarios, more than 50%) dispensable SVs are found. Furthermore, the experimental results also verify that the pruning method does not alter the SVMs' performances at all. As a corollary, this paper further contributes in theory a new lower upper bound on the number of SVs in the high-dimensional feature space.

Published
2008
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5. Complexity of error hypersurfaces in multilayer perceptrons.

Author

Liang X

Subjects
Algorithms, Models, Neurological, Models, Statistical, Artificial Intelligence, Neural Networks, Computer
Abstract

Error hypersurfaces are very valuable to study because of their unique status in multilayer perceptron research. Given the architecture of a multilayer perceptron, if the pattern sets are different, so are the respective error hypersurfaces in the multilayer perceptron. Using the theory of groups and Polya Theorem, this paper constructs classes of congruent pattern sets and classes of congruent error hypersurfaces, and proves that the number of classes of congruent pattern sets is equal to the number of congruent error hypersurfaces. Calculation results lead to much fewer classes of congruent error hypersurfaces than the total error hypersurfaces, and show that as the input dimension N increases, the former number increases at a much lower rate than the latter number, thus simplifying the understanding of the complexity of classes of error hypersurfaces.

Published
2004
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6. MVRACE: Multi-view Graph Contrastive Encoding for Graph Neural Network Pre-training

Author

Wu, Bo, Liang, Xun, Zheng, Xiangping, Guo, Yuhui, Zhang, Xuan, tang, hui, and Wang, Jun

Subjects
Artificial Intelligence, Machine learning, Pattern recognition, Computer-based experiment, Neural Networks
Abstract

Graph neural networks (GNNs) have become a defacto paradigm for graph representation learning. Generally, GNNs are trained in an end-to-end manner with supervision, requiring considerable task-specific labeled data. To reduce the labeling burden, recent works leverage self-supervised tasks to pre-train an expressive GNN model on abundant unlabeled data and finetune the trained model on downstream datasets with only a few labels. However, existing GNN pre-training approaches only concentrate on a single view for graph self-supervised learning while ignoring the rich semantic information in graphs, leading to the lack of sample utilization efficiency during the pre-training process. To tackle such challenges, we propose a multi-view graph contrastive encoding for graphs during GNN pre-training, called MVRACE. The critical insight is that we construct node and graph-level views to capture local attribute information and global structure in a graph. Concretely, the node-level view utilizes graph centrality and encodes the $r$-ego network to capture the local-whole relationship in a graph. The graph-level view aims to encode graph pairs to explore different graph structures and empower the discrimination ability of the GNN encoder. In addition, we combine multi-views with a joint contrastive loss function to integrate node- and graph-semantic information simultaneously. Comprehensive experiments on multiple domain datasets demonstrate that our approach can significantly yield competitive performance compared to state-of-the-art methods.

Published
2023

7. Mirror Turing Test: soul test based on poetry.

Author

Qi, Jinshan, Xue, Yang, Liang, Xun, and Feng, Zihuan

Subjects
TURING test, NATURAL language processing, ARTIFICIAL intelligence, MACHINE learning, MATHEMATICAL models, DEEP learning
Abstract

With the rapid development of machine intelligence, an increasing number of websites and servers have been amicably visited or sometimes attached by intelligent machines intensively. Therefore, how to empower a host machine to intelligently distinguish intelligent machines from humans is a challenging work. In this paper, the Mirror Turing Test (MTT) is conceived and implemented. Unlike the standard Turing Test, the tester in the MTT is replaced by a machine instead of a human. Current advancements on deep learning enable machines to recognize subtle differences between genuine and counterfeit works. Sometimes, the ability of machines is even superior to that of humans. Will machines transcend humans in an irreversible trend? Not completely right. The detection of soul in an artwork remains far beyond the capacity of machines. The two sets of MTT based on poetry generated by a machine and a novel imitated by a human were conducted in this paper and neither of them passed the MTT. Poetry is one of the art forms in which authors reveal their souls. Thus, we chose poetry in the MTT experiments on the basis of our soul computing model, thus clearly discriminating machine from human. [ABSTRACT FROM AUTHOR]

Published
2024
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8. Object tracking with collaborative extreme learning machines

Author

Liang Xun and Haipeng Kuang

Subjects
Computer Networks and Communications, Generalization, business.industry, Computer science, 020207 software engineering, 02 engineering and technology, Object (computer science), Tracking (particle physics), Machine learning, computer.software_genre, Expression (mathematics), Discriminative model, Hardware and Architecture, Video tracking, 0202 electrical engineering, electronic engineering, information engineering, Media Technology, Artificial intelligence, business, Representation (mathematics), computer, Software, Extreme learning machine
Abstract

We propose a novel collaborative discriminative model based on extreme learning machine (ELM) for object tracking in this paper. In order to represent the object more precisely, we first propose a new collaborative discriminative representation model, which includes both a global discriminative sub-model and a local discriminative sub-model. Different from traditional local representation models, in particular, our local sub-model integrates several classifiers which have structural relations to improve the expression. The global discriminative model represents the appearance comprehensively while the local discriminative sub-model can effectively address occlusions and assist the update. Second, to have better combination of these sub-models, we propose a novel collaboration strategy based on the Kullback-Leibler (KL) distance. The novel strategy can determine the weights of the sub-models adaptively by measuring their KL distances reciprocally. Third, we introduce ELM into tracking and adopt it to build both the global and the local discriminative sub-models simultaneously. Since ELM has a good generalization performance and is robust to the imbalance of the training samples, it is suitable to be used for tracking. Experimental results demonstrate that our method can achieve comparable performance to many state-of-the-art tracking approaches.

Published
2019

9. Research on Clustering Recognition Algorithm without any Prior Information

Author

Ze Xing Zhang, Liang Xun Shuo, and Yi Ying Chen

Subjects
k-medoids, Computer science, business.industry, Stability (learning theory), Pattern recognition, General Medicine, Random walk, Cluster (physics), Canopy clustering algorithm, Artificial intelligence, Recognition algorithm, business, Cluster analysis, k-medians clustering
Abstract

Aiming at the problem that the stability of clustering recognition algorithm without any prior information is not enough and the network cluster extraction quality needs to be improved and so on, the original algorithm is improved from the following aspects: The choice of the destination vertex function is added in the random walk; use automatic step number detection method to cancel the original algorithm of random walk steps parameter; add weight of evaluation inter-cluster connection on the original based on cluster extraction conditions; realize the controllable particle size of cluster extraction by introducing the threshold parameter. The test results show that the improved algorithm in terms of anti-jamming in the stability and the clustering quality are higher than the original algorithm.

Published
2013

10. Construction and Optimization of an Artificial Intelligence-Assisted Kansei Engineering Product Design Mapping Model

Author

Li, Tianxin, Li, Kan, Editor-in-Chief, Li, Qingyong, Associate Editor, Fournier-Viger, Philippe, Series Editor, Hong, Wei-Chiang, Series Editor, Liang, Xun, Series Editor, Wang, Long, Series Editor, Xu, Xuesong, Series Editor, Kuang, Yunshan, editor, Zhu, Lixin, editor, Zhang, Xiangyang, editor, and Khan, Intakhab Alam, editor

Published
2024
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11. Research on the Application of Blended Learning Mode Based on the OBE Philosophy --Taking 'Artificial Intelligence-Graph Search Strategy' as an Example

Author

Zhang, Bingge, Shao, Xiuting, Song, Ziwen, Li, Kan, Editor-in-Chief, Li, Qingyong, Associate Editor, Fournier-Viger, Philippe, Series Editor, Hong, Wei-Chiang, Series Editor, Liang, Xun, Series Editor, Wang, Long, Series Editor, Xu, Xuesong, Series Editor, Kuang, Yunshan, editor, Zhu, Lixin, editor, Zhang, Xiangyang, editor, and Khan, Intakhab Alam, editor

Published
2024
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12. A Survey of Current Status of Digital Reading Proficiency among Master's Students: A Case Study of a Normal University in Southwest China

Author

Jin, Xinru, Li, Kan, Editor-in-Chief, Li, Qingyong, Associate Editor, Fournier-Viger, Philippe, Series Editor, Hong, Wei-Chiang, Series Editor, Liang, Xun, Series Editor, Wang, Long, Series Editor, Xu, Xuesong, Series Editor, Kuang, Yunshan, editor, Zhu, Lixin, editor, Zhang, Xiangyang, editor, and Khan, Intakhab Alam, editor

Published
2024
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13. Research on the Teaching Mode of Artificial Intelligence Course Based on CDIO Concept : Taking Open Source Hardware as an Example

Author

Liu, Xiao, Luo, Ling, Ren, Fengmei, Li, Kan, Editor-in-Chief, Li, Qingyong, Associate Editor, Fournier-Viger, Philippe, Series Editor, Hong, Wei-Chiang, Series Editor, Liang, Xun, Series Editor, Wang, Long, Series Editor, Xu, Xuesong, Series Editor, Kuang, Yunshan, editor, Zhu, Lixin, editor, Zhang, Xiangyang, editor, and Khan, Intakhab Alam, editor

Published
2024
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15. Exploring AI-Interactive Teaching Models in the Context of Art-Science Integration: A Case Study on the 'Design Thinking' Course

Author

Han, Shaowen, Tian, Yuan, Li, Kan, Editor-in-Chief, Li, Qingyong, Associate Editor, Fournier-Viger, Philippe, Series Editor, Hong, Wei-Chiang, Series Editor, Liang, Xun, Series Editor, Wang, Long, Series Editor, Xu, Xuesong, Series Editor, Kuang, Yunshan, editor, Zhu, Lixin, editor, Zhang, Xiangyang, editor, and Khan, Intakhab Alam, editor

Published
2024
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17. The Random Forest Model for analyzing and Forecasting the US Stock Market under the background of smart finance

Author

Zheng, Jiajian, Xin, Duan, Cheng, Qishuo, Tian, Miao, Yang, Le, Li, Kan, Editor-in-Chief, Li, Qingyong, Associate Editor, Fournier-Viger, Philippe, Series Editor, Hong, Wei-Chiang, Series Editor, Liang, Xun, Series Editor, Wang, Long, Series Editor, Xu, Xuesong, Series Editor, Subramaniyam, Kannimuthu, editor, Leng, Lu, editor, Li, Jing, editor, and Wheeb, Ali Hussein, editor

Published
2024
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18. A Survey of Deep Reinforcement Learning in Financial Markets

Author

Yu, Ying, Li, Kan, Editor-in-Chief, Li, Qingyong, Associate Editor, Fournier-Viger, Philippe, Series Editor, Hong, Wei-Chiang, Series Editor, Liang, Xun, Series Editor, Wang, Long, Series Editor, Xu, Xuesong, Series Editor, Subramaniyam, Kannimuthu, editor, Leng, Lu, editor, Li, Jing, editor, and Wheeb, Ali Hussein, editor

Published
2024
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19. Study on Lifetime Prediction of Locking Block Based on Artificial Intelligence

Author

Cao, Jun, Gan, Jianghong, Guan, Hui, Li, Kan, Editor-in-Chief, Li, Qingyong, Associate Editor, Fournier-Viger, Philippe, Series Editor, Hong, Wei-Chiang, Series Editor, Liang, Xun, Series Editor, Wang, Long, Series Editor, Xu, Xuesong, Series Editor, Subramaniyam, Kannimuthu, editor, Leng, Lu, editor, Li, Jing, editor, and Wheeb, Ali Hussein, editor

Published
2024
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20. Correlation analysis of publication volume in abnormal behavior detection: A knowledge network perspective

Author

Wang, Shilong, Zhu, Jinghuan, Chao, Li, Li, Kan, Editor-in-Chief, Li, Qingyong, Associate Editor, Fournier-Viger, Philippe, Series Editor, Hong, Wei-Chiang, Series Editor, Liang, Xun, Series Editor, Wang, Long, Series Editor, Xu, Xuesong, Series Editor, Subramaniyam, Kannimuthu, editor, Leng, Lu, editor, Li, Jing, editor, and Wheeb, Ali Hussein, editor

Published
2024
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21. Research and analysis on the optimization of vocational education learning platform based on the background of artificial intelligence

Author

Wang, Liangyan, Dai, Yin, Jia, Lanxi, Li, Kan, Editor-in-Chief, Li, Qingyong, Associate Editor, Fournier-Viger, Philippe, Series Editor, Hong, Wei-Chiang, Series Editor, Liang, Xun, Series Editor, Wang, Long, Series Editor, Xu, Xuesong, Series Editor, Guan, Guiyun, editor, Kahl, Christian, editor, Majoul, Bootheina, editor, and Mishra, Deepanjali, editor

Published
2024
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22. The Application of Artificial Intelligence in Teacher Education in China

Author

Yang, Shuai, Li, Kan, Editor-in-Chief, Li, Qingyong, Associate Editor, Fournier-Viger, Philippe, Series Editor, Hong, Wei-Chiang, Series Editor, Liang, Xun, Series Editor, Wang, Long, Series Editor, Xu, Xuesong, Series Editor, Guan, Guiyun, editor, Kahl, Christian, editor, Majoul, Bootheina, editor, and Mishra, Deepanjali, editor

Published
2024
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23. Research on the Digital Competency Improvement Path of Higher Vocational UAV Application Technology Professional Teachers Based on AI Technology

Author

Wu, Jian, Mao, Zhifeng, Li, Kan, Editor-in-Chief, Li, Qingyong, Associate Editor, Fournier-Viger, Philippe, Series Editor, Hong, Wei-Chiang, Series Editor, Liang, Xun, Series Editor, Wang, Long, Series Editor, Xu, Xuesong, Series Editor, Guan, Guiyun, editor, Kahl, Christian, editor, Majoul, Bootheina, editor, and Mishra, Deepanjali, editor

Published
2024
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24. Research on Data Privacy Protection Strategies Based on Artificial Intelligence

Author

Yang, Jun, Ye, Qiukai, Han, Jiujiang, Xian, Ming, Li, Kan, Editor-in-Chief, Li, Qingyong, Associate Editor, Fournier-Viger, Philippe, Series Editor, Hong, Wei-Chiang, Series Editor, Liang, Xun, Series Editor, Wang, Long, Series Editor, Xu, Xuesong, Series Editor, Guan, Guiyun, editor, Kahl, Christian, editor, Majoul, Bootheina, editor, and Mishra, Deepanjali, editor

Published
2024
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26. Research on the Ideological and Political System of Logistics System Simulation Course in the Context of Artificial Intelligence

Author

Tang, Yan, Cui, Anqi, Hu, Yang, Li, Kan, Editor-in-Chief, Li, Qingyong, Associate Editor, Fournier-Viger, Philippe, Series Editor, Hong, Wei-Chiang, Series Editor, Liang, Xun, Series Editor, Wang, Long, Series Editor, Xu, Xuesong, Series Editor, Huang, Fang, editor, Zhan, Zehui, editor, Khan, Intakhab Alam, editor, and Birkök, Mehmet Cüneyt, editor

Published
2024
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27. Design and Research of Blended Teaching Mode Based on Artificial Intelligence

Author

Shan, Yingying, Li, Kan, Editor-in-Chief, Li, Qingyong, Associate Editor, Fournier-Viger, Philippe, Series Editor, Hong, Wei-Chiang, Series Editor, Liang, Xun, Series Editor, Wang, Long, Series Editor, Xu, Xuesong, Series Editor, Huang, Fang, editor, Zhan, Zehui, editor, Khan, Intakhab Alam, editor, and Birkök, Mehmet Cüneyt, editor

Published
2024
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28. Design of Monitoring Platform for Communication Operation and Maintenance of Distribution Network Based on Artificial Intelligence Technology

Author

Chen, Jia, Liu, Guoying, Weng, Junhong, Li, Kan, Editor-in-Chief, Li, Qingyong, Associate Editor, Fournier-Viger, Philippe, Series Editor, Hong, Wei-Chiang, Series Editor, Liang, Xun, Series Editor, Wang, Long, Series Editor, Xu, Xuesong, Series Editor, Huang, Fang, editor, Zhan, Zehui, editor, Khan, Intakhab Alam, editor, and Birkök, Mehmet Cüneyt, editor

Published
2024
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33. The Innovative Path of Intelligent Teaching in the Age of Artificial Intelligence

Author

Liu, Ye, Wang, Shuai, Liu, Jun, Qu, Shaohui, Li, Kan, Editor-in-Chief, Li, Qingyong, Associate Editor, Fournier-Viger, Philippe, Series Editor, Hong, Wei-Chiang, Series Editor, Liang, Xun, Series Editor, Wang, Long, Series Editor, Xu, Xuesong, Series Editor, Huang, Fang, editor, Zhan, Zehui, editor, Khan, Intakhab Alam, editor, and Birkök, Mehmet Cüneyt, editor

Published
2024
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34. Prediction and Analysis of China’s Non Sovereign Digital Currency Demand Based on Big Data and Artificial Intelligence

Author

Zhang, Chen, Li, Kan, Editor-in-Chief, Li, Qingyong, Associate Editor, Fournier-Viger, Philippe, Series Editor, Hong, Wei-Chiang, Series Editor, Liang, Xun, Series Editor, Wang, Long, Series Editor, Xu, Xuesong, Series Editor, Chen, Charles, editor, Singh, Satya Narayan, editor, Saxena, Sandeep, editor, and Wheeb, Ali Hussein, editor

Published
2023
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35. Research on the application of Artificial Intelligence in Public Administration

Author

Liu, Xitao, Han, Yuchen, Li, Kan, Editor-in-Chief, Li, Qingyong, Associate Editor, Fournier-Viger, Philippe, Series Editor, Hong, Wei-Chiang, Series Editor, Liang, Xun, Series Editor, Wang, Long, Series Editor, Xu, Xuesong, Series Editor, Chen, Charles, editor, Singh, Satya Narayan, editor, Saxena, Sandeep, editor, and Wheeb, Ali Hussein, editor

Published
2023
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38. Training of Skilled Talents Based on Artificial Intelligence

Author

Zhang, Xing, Wu, Jun, Hu, Shayu, Li, Kan, Editor-in-Chief, Li, Qingyong, Associate Editor, Fournier-Viger, Philippe, Series Editor, Hong, Wei-Chiang, Series Editor, Liang, Xun, Series Editor, Wang, Long, Series Editor, Xu, Xuesong, Series Editor, Yuan, Xueming, editor, Kurniawan, Yohannes, editor, and Ji, Zhenyan, editor

Published
2023
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41. Application of Information Technology in Research & Development of AIoT Energy Curriculum

Author

Lin, Amy, Lai, Yin-Kuei, Shiao, Ruey-Chang, Lin, Fanny, Lin, Louis, Li, Kan, Editor-in-Chief, Li, Qingyong, Associate Editor, Fournier-Viger, Philippe, Series Editor, Hong, Wei-Chiang, Series Editor, Liang, Xun, Series Editor, Wang, Long, Series Editor, Xu, Xuesong, Series Editor, Yuan, Xueming, editor, Kurniawan, Yohannes, editor, and Ji, Zhenyan, editor

Published
2023
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49. A Review of Artificial Intelligence Risks in Social Science Research

Author

Du, Yan, Yuan, Chihping, Li, Kan, Editor-in-Chief, Li, Qingyong, Associate Editor, Fournier-Viger, Philippe, Series Editor, Hong, Wei-Chiang, Series Editor, Liang, Xun, Series Editor, Wang, Long, Series Editor, Xu, Xuesong, Series Editor, Luqman, Adeel, editor, Li, Hongbo, editor, and Ali, Ghaffar, editor

Published
2023
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50. Impact factor analysis report of paper acceptance at ICLR

Author

Hu, Shuangyang, Li, Kan, Editor-in-Chief, Li, Qingyong, Associate Editor, Fournier-Viger, Philippe, Series Editor, Hong, Wei-Chiang, Series Editor, Liang, Xun, Series Editor, Wang, Long, Series Editor, Xu, Xuesong, Series Editor, Fernández-Viagas, Víctor, editor, Kandel, Bijay Kumar, editor, Valmohammadi, Changiz, editor, and Mallick, Hrushikesh, editor

Published
2023
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