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| 1 | A kernel learning framework for domain adaptation learning显示文摘Domain adaptation learning(DAL) methods have shown promising results by utilizing labeled samples from the source(or auxiliary) domain(s) to learn a robust classifier for the target domain which has a few or even no labeled samples.However,there exist several key issues which need to be addressed in the state-of-theart DAL methods such as sufficient and effective distribution discrepancy metric learning,effective kernel space learning,and multiple source domains transfer learning,etc.Aiming at the mentioned-above issues,in this paper,we propose a unified kernel learning framework for domain adaptation learning and its effective extension based on multiple kernel learning(MKL) schema,regularized by the proposed new minimum distribution distance metric criterion which minimizes both the distribution mean discrepancy and the distribution scatter discrepancy between source and target domains,into which many existing kernel methods(like support vector machine(SVM),v-SVM,and least-square SVM) can be readily incorporated.Our framework,referred to as kernel learning for domain adaptation learning(KLDAL),simultaneously learns an optimal kernel space and a robust classifier by minimizing both the structural risk functional and the distribution discrepancy between different domains.Moreover,we extend the framework KLDAL to multiple kernel learning framework referred to as MKLDAL.Under the KLDAL or MKLDAL framework,we also propose three effective formulations called KLDAL-SVM or MKLDAL-SVM with respect to SVM and its variant μ-KLDALSVM or μ-MKLDALSVM with respect to v-SVM,and KLDAL-LSSVM or MKLDAL-LSSVM with respect to the least-square SVM,respectively.Comprehensive experiments on real-world data sets verify the outperformed or comparable effectiveness of the proposed frameworks. | TAO JianWen CHUNG FuLai WANG ShiTong | 2012 | Science China(Information Sciences)2012，55，9： | 10 |
| 2 | Generalized fuzzy c-means clustering algorithm withimproved fuzzy partitions显示文摘 | ZHU LIN CHUNG FULAI WANG SHITONG | 2009 | IEEE Transactions on Systems Man and Cybernetics Part B:Cybernetics2009，39，3： | 1 |
| 3 | On minimum dis- tribution discrepancy support vector machine for domain adaotation显示文摘 | Tao Jianwen Chung Fulai Wang Shitong | 2012 | Pattern Recognition2012，45，11： | 1 |
| 4 | Enhanced soft sub-space clustering intergrating within-cluster and between-cluster infor-mation 显示文摘 | Deng Zhaohong Choi Kupsze Chung Fulai | 2010 | Pattern Recognition2010，43，3： | 1 |
| 5 | On minimum class locality preserving variance support vector machine显示文摘 | WANG Xiaoming CHUNG Fulai WANG Shitong | 2010 | Patter recognition2010，43，8： | 1 |
| 6 | Enhanced soft subspace clustering integrating within-cluster and between-cluster information显示文摘 | DENG Zhaohong CHOI K S CHUNG Fulai | 2010 | Pattern recognition2010，43，3： | 1 |
| 7 | Theoretical choice of the optimal threshold for possibilistic linear model with noisy input显示文摘 | Ge Hongwei Chung Fulai Wang Shitong | 2008 | IEEE Transactions on Fuzzy Systems2008，16，4： | 1 |
| 8 | Genemlized fuzzy C-Means clustering algorithm with improved fuzzy partitions显示文摘 | Zhu Lin Chung Fulai Wang Shitong | 2009 | IEEE Transactions on Systerms Man and Cybernetics:Part B2009，39，3： | 1 |
| 9 | FRSDE:FastReduced Set Density Estimator Using Minimal Enclosing Ball显示文摘 | Deng Zhaohong Chung Fulai Wang Shitong | 2008 | Pattern Recognition2008，41，4： | 1 |
| 10 | From minimum enclosing ball to fast fuzzy inference system training on large datasets显示文摘 | Chung Fulai Deng Zhaohong Wang Shitong | 2009 | IEEE Transactions on Fuzzy Systems2009，17，1： | 1 |
| 11 | From minimum enclosing ball to fast fuzzy inference system training on large datasets显示文摘 | Chung Fulai Deng Zhaohong Wang Shitong | 2009 | IEEE Trans on Fuzzy System2009，，： | 1 |
| 12 | A novel image thresholding method based on Parzen window estimate 显示文摘 | Shitong Wang Fulai Chung Fusong Xiong | 2008 | Pattern Recognition2008，41，1： | 1 |
| 13 | En- hanced Soft Subspace Clustering Integrating within Cluster and Between-cluster Information显示文摘 | Deng Zhaohong Choi K S Chung Fulai | 2010 | Pattern Recognition2010，43，3： | 1 |
| 14 | Generalized Fuzzy C-Means Cluste- ring Algorithm with Improved Fuzzy Partitions 显示文摘 | Zhu Lin Chung Fulai Wang Shitong | 2009 | IEEE Transactions on Systems Man and Cybernetics Part B-cybernetics2009，39，3： | 1 |
| 15 | Fragile watermarking scheme for image authentication显示文摘 | Liu Hongtao Shen Ruiming Chung Fulai | | 0，，12： | 1 |
| 16 | Generalized fuzzy C-means clustering algorithm with improved fuzzy partitions显示文摘 | ZHU Lin CHUNG Fulai and WANG Shitong | 2009 | IEEE Transactions on Systems Man and Cybernetics2009，39，3： | 1 |
| 17 | A Novel Image Thresholding Method Based on Parzen Window Estimate显示文摘 | Wang Shitong Chung Fulai Xiong Fusong | 2008 | Pattern Recognition2008，41，1： | 1 |
| 18 | Fuzzy Competitive Learning显示文摘 | FULAI CHUNG TONG LEE | 1994 | Neural Networks1994，7，1： | 1 |
| 19 | Theoretically Optimal Parameter Choices for Support Vector Regression Machines with Noisy Input显示文摘 | Wang Shitong Zhu Jiagang Chung Fulai | 2005 | Soft Computing2005，9，10： | 1 |
| 20 | Kernel density estimation,kernel methods,and fast learning in large data sets显示文摘 | Wang Shitong Wang Jun Chung Fulai | 2014 | IEEE Trans on Cybernetics2014，44，1： | 1 |
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