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James Tin-Yau Kwok
James T. Kwok
2010 – today
- 2013
[i2]Yu-Feng Li, Ivor W. Tsang, James T. Kwok, Zhi-Hua Zhou: Convex and Scalable Weakly Labeled SVMs. CoRR abs/1303.1271 (2013)- 2012
- [j45]Liqing Zhang, James Tin-Yau Kwok, Changshui Zhang: A brief introduction to the special issue for ISNN2010. Neurocomputing 76(1): 1 (2012)
- [j44]Jian-Hua Zhao, Philip L. H. Yu, James T. Kwok: Bilinear Probabilistic Principal Component Analysis. IEEE Trans. Neural Netw. Learning Syst. 23(3): 492-503 (2012)
- [j43]Leon Wenliang Zhong, James T. Kwok: Efficient Sparse Modeling With Automatic Feature Grouping. IEEE Trans. Neural Netw. Learning Syst. 23(9): 1436-1447 (2012)
- [c66]
- [c65]Wenliang Zhong, James Tin-Yau Kwok: Convex Multitask Learning with Flexible Task Clusters. ICML 2012
- [c64]
- [i1]Wenliang Zhong, James Tin-Yau Kwok: Convex Multitask Learning with Flexible Task Clusters. CoRR abs/1206.4601 (2012)
- 2011
- [j42]Wen-Yun Yang, Bao-Liang Lu, James T. Kwok: Incorporating cellular sorting structure for better prediction of protein subcellular locations. J. Exp. Theor. Artif. Intell. 23(1): 79-95 (2011)
- [j41]Sinno Jialin Pan, Ivor W. Tsang, James T. Kwok, Qiang Yang: Domain Adaptation via Transfer Component Analysis. IEEE Transactions on Neural Networks 22(2): 199-210 (2011)
- [j40]Shutao Li, Mingkui Tan, Ivor W. Tsang, James Tin-Yau Kwok: A Hybrid PSO-BFGS Strategy for Global Optimization of Multimodal Functions. IEEE Transactions on Systems, Man, and Cybernetics, Part B 41(4): 1003-1014 (2011)
- [c63]Mu Li, Xiao-Chen Lian, James T. Kwok, Bao-Liang Lu: Time and space efficient spectral clustering via column sampling. CVPR 2011: 2297-2304
- [c62]Wenliang Zhong, James T. Kwok: Efficient Sparse Modeling with Automatic Feature Grouping. ICML 2011: 9-16
- [c61]
- [c60]Weike Pan, James T. Kwok: Structured clustering with automatic kernel adaptation. IJCNN 2011: 1322-1327
- [e7]Bao-Liang Lu, Liqing Zhang, James T. Kwok (Eds.): Neural Information Processing - 18th International Conference, ICONIP 2011, Shanghai, China, November 13-17, 2011, Proceedings, Part I. Lecture Notes in Computer Science 7062, Springer 2011, ISBN 978-3-642-24954-9
- [e6]Bao-Liang Lu, Liqing Zhang, James T. Kwok (Eds.): Neural Information Processing - 18th International Conference, ICONIP 2011, Shanghai, China, November 13-17, 2011, Proceedings, Part II. Lecture Notes in Computer Science 7063, Springer 2011, ISBN 978-3-642-24957-0
- [e5]Bao-Liang Lu, Liqing Zhang, James T. Kwok (Eds.): Neural Information Processing - 18th International Conference, ICONIP 2011, Shanghai, China, November 13-17, 2011, Proceedings, Part III. Lecture Notes in Computer Science 7064, Springer 2011, ISBN 978-3-642-24964-8
- 2010
- [j39]Ming Zhao, Shutao Li, James Tin-Yau Kwok: Text detection in images using sparse representation with discriminative dictionaries. Image Vision Comput. 28(12): 1590-1599 (2010)
- [j38]Yan-xia Jin, Kai Zhang, James T. Kwok, Han-chang Zhou: Fast and accurate kernel density approximation using a divide-and-conquer approach. Journal of Zhejiang University - Science C 11(9): 677-689 (2010)
- [j37]Kai Zhang, James T. Kwok: Simplifying mixture models through function approximation. IEEE Transactions on Neural Networks 21(4): 644-658 (2010)
- [j36]Wenliang Zhong, Weike Pan, James T. Kwok, Ivor W. Tsang: Incorporating the loss function into discriminative clustering of structured outputs. IEEE Transactions on Neural Networks 21(10): 1564-1575 (2010)
- [j35]Kai Zhang, James T. Kwok: Clustered Nyström method for large scale manifold learning and dimension reduction. IEEE Transactions on Neural Networks 21(10): 1576-1587 (2010)
- [c59]Yu-Feng Li, James T. Kwok, Zhi-Hua Zhou: Cost-Sensitive Semi-Supervised Support Vector Machine. AAAI 2010
- [c58]Mu Li, James T. Kwok, Bao-Liang Lu: Online multiple instance learning with no regret. CVPR 2010: 1395-1401
- [c57]Mu Li, James T. Kwok, Bao-Liang Lu: Making Large-Scale Nyström Approximation Possible. ICML 2010: 631-638
- [c56]Chonghai Hu, James T. Kwok: Manifold regularization for structured outputs via the joint kernel. IJCNN 2010: 1-8
- [c55]Wen-Yun Yang, James T. Kwok, Bao-Liang Lu: Spectral and Semidefinite Relaxation of the CLUHSIC Algorithm. SDM 2010: 106-117
- [e4]Liqing Zhang, Bao-Liang Lu, James Tin-Yau Kwok (Eds.): Advances in Neural Networks - ISNN 2010, 7th International Symposium on Neural Networks, ISNN 2010, Shanghai, China, June 6-9, 2010, Proceedings, Part I. Lecture Notes in Computer Science 6063, Springer 2010, ISBN 978-3-642-13277-3
- [e3]Liqing Zhang, Bao-Liang Lu, James Tin-Yau Kwok (Eds.): Advances in Neural Networks - ISNN 2010, 7th International Symposium on Neural Networks, ISNN 2010, Shanghai, China, June 6-9, 2010, Proceedings, Part II. Lecture Notes in Computer Science 6064, Springer 2010, ISBN 978-3-642-13317-6
2000 – 2009
- 2009
- [j34]Yu-Feng Li, Ivor W. Tsang, James Tin-Yau Kwok, Zhi-Hua Zhou: Tighter and Convex Maximum Margin Clustering. Journal of Machine Learning Research - Proceedings Track 5: 344-351 (2009)
- [j33]Kai Zhang, James T. Kwok: Density-Weighted Nyström Method for Computing Large Kernel Eigensystems. Neural Computation 21(1): 121-146 (2009)
- [j32]Brian Kan-Wing Mak, Tsz-Chung Lai, Ivor W. Tsang, James Tin-Yau Kwok: Maximum Penalized Likelihood Kernel Regression for Fast Adaptation. IEEE Transactions on Audio, Speech & Language Processing 17(7): 1372-1381 (2009)
- [j31]Kai Zhang, Ivor W. Tsang, James T. Kwok: Maximum Margin Clustering Made Practical. IEEE Transactions on Neural Networks 20(4): 583-596 (2009)
- [j30]Mingqing Hu, Yiqiang Chen, James Tin-Yau Kwok: Building Sparse Multiple-Kernel SVM Classifiers. IEEE Transactions on Neural Networks 20(5): 827-839 (2009)
- [c54]Bin Zhao, James Tin-Yau Kwok, Fei Wang, Changshui Zhang: Unsupervised Maximum Margin Feature Selection with manifold regularization. CVPR 2009: 888-895
- [c53]Bin Zhao, James Tin-Yau Kwok, Changshui Zhang: Maximum Margin Clustering with Multivariate Loss Function. ICDM 2009: 637-646
- [c52]Xi Chen, Weike Pan, James T. Kwok, Jaime G. Carbonell: Accelerated Gradient Method for Multi-task Sparse Learning Problem. ICDM 2009: 746-751
- [c51]
- [c50]Kai Zhang, James T. Kwok, Bahram Parvin: Prototype vector machine for large scale semi-supervised learning. ICML 2009: 155
- [c49]Sinno Jialin Pan, Ivor W. Tsang, James T. Kwok, Qiang Yang: Domain Adaptation via Transfer Component Analysis. IJCAI 2009: 1187-1192
- [c48]Chonghai Hu, James T. Kwok, Weike Pan: Accelerated Gradient Methods for Stochastic Optimization and Online Learning. NIPS 2009: 781-789
- [c47]Yu-Feng Li, James T. Kwok, Ivor W. Tsang, Zhi-Hua Zhou: A Convex Method for Locating Regions of Interest with Multi-instance Learning. ECML/PKDD (2) 2009: 15-30
- [c46]
- 2008
- [j29]Ivor Wai-Hung Tsang, András Kocsor, James Tin-Yau Kwok: Large-Scale Maximum Margin Discriminant Analysis Using Core Vector Machines. IEEE Transactions on Neural Networks 19(4): 610-624 (2008)
- [j28]Xianchao Xie, Shuicheng Yan, James T. Kwok, Thomas S. Huang: Matrix-Variate Factor Analysis and Its Applications. IEEE Transactions on Neural Networks 19(10): 1821-1826 (2008)
- [c45]Sinno Jialin Pan, James T. Kwok, Qiang Yang: Transfer Learning via Dimensionality Reduction. AAAI 2008: 677-682
- [c44]Sinno Jialin Pan, Dou Shen, Qiang Yang, James T. Kwok: Transferring Localization Models across Space. AAAI 2008: 1383-1388
- [c43]Kai Zhang, Ivor W. Tsang, James T. Kwok: Improved Nyström low-rank approximation and error analysis. ICML 2008: 1232-1239
- [e2]Niels da Vitoria Lobo, Takis Kasparis, Fabio Roli, James Tin-Yau Kwok, Michael Georgiopoulos, Georgios C. Anagnostopoulos, Marco Loog (Eds.): Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop, SSPR & SPR 2008, Orlando, USA, December 4-6, 2008. Proceedings. Lecture Notes in Computer Science 5342, Springer 2008, ISBN 978-3-540-89688-3
- 2007
- [j27]Patrick C. K. Hung, Dickson K. W. Chiu, W. W. Fung, William K. Cheung, Raymond K. Wong, Samuel P. M. Choi, Eleanna Kafeza, James T. Kwok, Joshua C. C. Pun, Vivying S. Y. Cheng: End-to-end privacy control in service outsourcing of human intensive processes: A multi-layered Web service integration approach. Information Systems Frontiers 9(1): 85-101 (2007)
- [j26]Zhihua Zhang, James T. Kwok, Dit-Yan Yeung: Surrogate maximization/minimization algorithms and extensions. Machine Learning 69(1): 1-33 (2007)
- [j25]Jooyoung Park, Daesung Kang, Jongho Kim, James T. Kwok, Ivor W. Tsang: SVDD-Based Pattern Denoising. Neural Computation 19(7): 1919-1938 (2007)
- [j24]Fei Wang, Jingdong Wang, Changshui Zhang, James T. Kwok: Face recognition using spectral features. Pattern Recognition 40(10): 2786-2797 (2007)
- [j23]James T. Kwok, Ivor Wai-Hung Tsang, Jacek M. Zurada: A Class of Single-Class Minimax Probability Machines for Novelty Detection. IEEE Transactions on Neural Networks 18(3): 778-785 (2007)
- [c42]Sinno Jialin Pan, James T. Kwok, Qiang Yang, Jeffrey Junfeng Pan: Adaptive Localization in a Dynamic WiFi Environment through Multi-view Learning. AAAI 2007: 1108-1113
- [c41]Ivor W. Tsang, András Kocsor, James T. Kwok: Simpler core vector machines with enclosing balls. ICML 2007: 911-918
- [c40]Kai Zhang, Ivor W. Tsang, James T. Kwok: Maximum margin clustering made practical. ICML 2007: 1119-1126
- [c39]
- [c38]Ivor W. Tsang, James T. Kwok: Ensembles of Partially Trained SVMs with Multiplicative Updates. IJCAI 2007: 1089-1094
- 2006
- [j22]Zhihua Zhang, James T. Kwok, Dit-Yan Yeung: Model-based transductive learning of the kernel matrix. Machine Learning 63(1): 69-101 (2006)
- [j21]Brian Kan-Wing Mak, Roger Wend-Huu Hsiao, Simon Ka-Lung Ho, James T. Kwok: Embedded kernel eigenvoice speaker adaptation and its implication to reference speaker weighting. IEEE Transactions on Audio, Speech & Language Processing 14(4): 1267-1280 (2006)
- [j20]Jeffrey Junfeng Pan, James T. Kwok, Qiang Yang, Yiqiang Chen: Multidimensional Vector Regression for Accurate and Low-Cost Location Estimation in Pervasive Computing. IEEE Trans. Knowl. Data Eng. 18(9): 1181-1193 (2006)
- [j19]Ivor Wai-Hung Tsang, James Tin-Yau Kwok: Efficient hyperkernel learning using second-order cone programming. IEEE Transactions on Neural Networks 17(1): 48-58 (2006)
- [j18]Ivor Wai-Hung Tsang, James Tin-Yau Kwok, Jacek M. Zurada: Generalized Core Vector Machines. IEEE Transactions on Neural Networks 17(5): 1126-1140 (2006)
- [c37]Kai Zhang, James T. Kwok, Ming Tang: Accelerated Convergence Using Dynamic Mean Shift. ECCV (2) 2006: 257-268
- [c36]Ivor W. Tsang, András Kocsor, James T. Kwok: Diversified SVM Ensembles for Large Data Sets. ECML 2006: 792-800
- [c35]Jooyoung Park, Daesung Kang, James T. Kwok, Sang-Woong Lee, Bon-Woo Hwang, Seong-Whan Lee: Facial Image Reconstruction by SVDD-Based Pattern De-noising. ICB 2006: 129-135
- [c34]Pak-Ming Cheung, James T. Kwok: A regularization framework for multiple-instance learning. ICML 2006: 193-200
- [c33]Juan Dai, Shuicheng Yan, Xiaoou Tang, James T. Kwok: Locally adaptive classification piloted by uncertainty. ICML 2006: 225-232
- [c32]
- [c31]Shutao Li, Chen Liao, James T. Kwok: Gene Feature Extraction Using T-Test Statistics and Kernel Partial Least Squares. ICONIP (3) 2006: 11-20
- [c30]Shutao Li, Jinglin Peng, James T. Kwok, Jing Zhang: Multimodal Registration using the Discrete Wavelet Frame Transform. ICPR (3) 2006: 877-880
- [c29]Ivor W. Tsang, James T. Kwok, Shutao Li: Learning the Kernel in Mahalanobis One-Class Support Vector Machines. IJCNN 2006: 1169-1175
- [c28]Ken Chen, Bao-Liang Lu, James T. Kwok: Efficient Classification of Multi-label and Imbalanced Data using Min-Max Modular Classifiers. IJCNN 2006: 1770-1775
- [c27]Shutao Li, Chen Liao, James T. Kwok: Wavelet-Based Feature Extraction for Microarray Data Classification. IJCNN 2006: 5028-5033
- [c26]Ivor W. Tsang, András Kocsor, James T. Kwok: Efficient kernel feature extraction for massive data sets. KDD 2006: 724-729
- [c25]
- [c24]
- [e1]Dit-Yan Yeung, James T. Kwok, Ana L. N. Fred, Fabio Roli, Dick de Ridder (Eds.): Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshops, SSPR 2006 and SPR 2006, Hong Kong, China, August 17-19, 2006, Proceedings. Lecture Notes in Computer Science 4109, Springer 2006, ISBN 3-540-37236-9
- 2005
- [j17]Ivor W. Tsang, James T. Kwok, Pak-Ming Cheung: Core Vector Machines: Fast SVM Training on Very Large Data Sets. Journal of Machine Learning Research 6: 363-392 (2005)
- [j16]Brian Mak, James Tin-Yau Kwok, Simon Ka-Lung Ho: Kernel Eigenvoice Speaker Adaptation. IEEE Transactions on Speech and Audio Processing 13(5-2): 984-992 (2005)
- [c23]Patrick C. K. Hung, Dickson K. W. Chiu, W. W. Fung, William K. Cheung, Raymond K. Wong, Samuel P. M. Choi, Eleanna Kafeza, James T. Kwok, Joshua C. C. Pun, Vivying S. Y. Cheng: Towards end-to-end privacy control in the outsourcing of marketing activities: a web service integration solution. ICEC 2005: 454-461
- [c22]Kai Zhang, Ming Tang, James T. Kwok: Applying Neighborhood Consistency for Fast Clustering and Kernel Density Estimation. CVPR (2) 2005: 1001-1007
- [c21]Kin Fung Simon Wong, Ivor W. Tsang, Victor Cheung, S.-H. Gary Chan, James T. Kwok: Position estimation for wireless sensor networks. GLOBECOM 2005: 5
- [c20]Ivor W. Tsang, James T. Kwok, Kimo T. Lai: Core Vector Regression for very large regression problems. ICML 2005: 912-919
- [c19]Jeffrey Junfeng Pan, James T. Kwok, Qiang Yang, Yiqiang Chen: Accurate and Low-cost Location Estimation Using Kernels. IJCAI 2005: 1366-1371
- 2004
- [j15]Victor Cheng, Chun Hung Li, James T. Kwok, Chi-Kwong Li: Dissimilarity learning for nominal data. Pattern Recognition 37(7): 1471-1477 (2004)
- [j14]James Tin-Yau Kwok, Ivor Wai-Hung Tsang: The pre-image problem in kernel methods. IEEE Transactions on Neural Networks 15(6): 1517-1525 (2004)
- [j13]Shutao Li, James Tin-Yau Kwok, Ivor Wai-Hung Tsang, Yaonan Wang: Fusing images with different focuses using support vector machines. IEEE Transactions on Neural Networks 15(6): 1555-1561 (2004)
- [c18]Zhihua Zhang, Kap Luk Chan, James T. Kwok, Dit-Yan Yeung: Bayesian Inference on Principal Component Analysis Using Reversible Jump Markov Chain Monte Carlo. AAAI 2004: 372-377
- [c17]Ivor W. Tsang, James T. Kwok: Efficient Hyperkernel Learning Using Second-Order Cone Programming. ECML 2004: 453-464
- [c16]Haitao Zhao, Pong Chi Yuen, James T. Kwok, Jingyu Yang: Incremental PCA based face recognition. ICARCV 2004: 687-691
- [c15]Zhihua Zhang, James T. Kwok, Dit-Yan Yeung: Surrogate maximization/minimization algorithms for AdaBoost and the logistic regression model. ICML 2004
- [c14]Zhihua Zhang, Dit-Yan Yeung, James T. Kwok: Bayesian inference for transductive learning of kernel matrix using the Tanner-Wong data augmentation algorithm. ICML 2004
- [c13]Brian Mak, Simon Ho, James T. Kwok: Speedup of kernel eigenvoice speaker adaptation by embedded kernel PCA. INTERSPEECH 2004
- 2003
- [j12]Kwok-Wai Cheung, James T. Kwok, Martin H. C. Law, Kwok Ching Tsui: Mining customer product ratings for personalized marketing. Decision Support Systems 35(2): 231-243 (2003)
- [j11]Shutao Li, James T. Kwok, Hailong Zhu, Yaonan Wang: Texture classification using the support vector machines. Pattern Recognition 36(12): 2883-2893 (2003)
- [j10]James T. Kwok, Ivor W. Tsang: Linear dependency between ε and the input noise in ε-support vector regression. IEEE Transactions on Neural Networks 14(3): 544-553 (2003)
- [c12]
- [c11]
- [c10]Zhihua Zhang, James T. Kwok, Dit-Yan Yeung: Parametric Distance Metric Learning with Label Information. IJCAI 2003: 1450-
- [c9]
- 2002
- [j9]Shutao Li, James T. Kwok, Yaonan Wang: Using the discrete wavelet frame transform to merge Landsat TM and SPOT panchromatic images. Information Fusion 3(1): 17-23 (2002)
- [j8]Shutao Li, James T. Kwok, Yaonan Wang: Multifocus image fusion using artificial neural networks. Pattern Recognition Letters 23(8): 985-997 (2002)
- [c8]Shutao Li, James T. Kwok, Yaonan Wang: Fusing Images with Multiple Focuses Using Support Vector Machines. ICANN 2002: 1287-1292
- [c7]Hailong Zhu, James T. Kwok, Liangsheng Qu: Improving De-Noising by Coefficient De-Noising and Dyadic Wavelet Transform. ICPR (2) 2002: 273-
- 2001
- [j7]Shutao Li, James T. Kwok, Yaonan Wang: Combination of images with diverse focuses using the spatial frequency. Information Fusion 2(3): 169-176 (2001)
- [c6]Martin H. C. Law, James T. Kwok: Applying the Bayesian Evidence Framework to \nu -Support Vector Regression. ECML 2001: 312-323
- [c5]James T. Kwok: Linear Dependency between epsilon and the Input Noise in epsilon-Support Vector Regression. ICANN 2001: 405-410
- 2000
- [j6]James Tin-Yau Kwok: The evidence framework applied to support vector machines. IEEE Trans. Neural Netw. Learning Syst. 11(5): 1162-1173 (2000)
- [c4]Martin H. C. Law, James T. Kwok: Rival Penalized Competitive Learning for Model-Based Sequence Clustering. ICPR 2000: 2195-2198
1990 – 1999
- 1999
- [j5]James Tin-Yau Kwok: Moderating the outputs of support vector machine classifiers. IEEE Transactions on Neural Networks 10(5): 1018-1031 (1999)
- [c3]James Tin-Yau Kwok: Integrating the evidence framework and the support vector machine. ESANN 1999: 177-182
- 1998
- [c2]James Tin-Yau Kwok: Automated Text Categorization Using Support Vector Machine. ICONIP 1998: 347-351
- 1997
- [j4]James Tin-Yau Kwok, Dit-Yan Yeung: Constructive algorithms for structure learning in feedforward neural networks for regression problems. IEEE Trans. Neural Netw. Learning Syst. 8(3): 630-645 (1997)
- [j3]James Tin-Yau Kwok, Dit-Yan Yeung: Objective functions for training new hidden units in constructive neural networks. IEEE Trans. Neural Netw. Learning Syst. 8(5): 1131-1148 (1997)
- 1996
- [j2]James Tin-Yau Kwok, Dit-Yan Yeung: Use of bias term in projection pursuit learning improves approximation and convergence properties. IEEE Trans. Neural Netw. Learning Syst. 7(5): 1168-1183 (1996)
- [c1]James Tin-Yau Kwok, Dit-Yan Yeung: Bayesian Regularization in Constructive Neural Networks. ICANN 1996: 557-562
- 1995
- [j1]James Tin-Yau Kwok, Dit-Yan Yeung: Improving the approximation and convergence capabilities of projection pursuit learning. Neural Processing Letters 2(3): 20-25 (1995)
Coauthor Index
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last updated on 2013-04-09 21:25 CEST by the dblp team