Kwok, James Tin-Yau - Department of Computer Science, Hong Kong University of Science and Technology

• Mach Learn (2007) 69: 133 DOI 10.1007/s10994-007-5022-x
• Large-Scale Sparsified Manifold Regularization Ivor W. Tsang James T. Kwok
• Accelerated Convergence Using Dynamic Mean Shift
• Improved Nystrom Low-Rank Approximation and Error Analysis Kai Zhang TWINSEN@CSE.UST.HK
• IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 20, NO. 4, APRIL 2009 583 Maximum Margin Clustering Made Practical
• Transfer Learning via Dimensionality Reduction Sinno Jialin Pan, James T. Kwok and Qiang Yang
• 1372 IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 17, NO. 7, SEPTEMBER 2009 Maximum Penalized Likelihood Kernel
• 1126 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 17, NO. 5, SEPTEMBER 2006 Generalized Core Vector Machines
• Text detection in images using sparse representation with discriminative dictionaries Ming Zhao a
• 1576 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 21, NO. 10, OCTOBER 2010 Clustered Nystrom Method for Large Scale
• IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 20, NO. 5, MAY 2009 827 Building Sparse Multiple-Kernel SVM Classifiers
• LETTER Communicated by Garrison Cottrell Density-Weighted Nystrom Method for Computing
• Sliced Coordinate Analysis for Effective Dimension Reduction and Nonlinear Extensions
• Pattern Recognition 40 (2007) 27862797 www.elsevier.com/locate/pr
• LETTER Communicated by Klaus-Robert Muller SVDD-Based Pattern Denoising
• 778 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 18, NO. 3, MAY 2007 A Class of Single-Class Minimax Probability
• IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS--PART B: CYBERNETICS, VOL. 36, NO. 4, AUGUST 2006 873 A Novel Incremental Principal Component Analysis
• IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 14, NO. 4, JULY 2006 1267 Embedded Kernel Eigenvoice Speaker Adaptation
• 48 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 17, NO. 1, JANUARY 2006 Efficient Hyperkernel Learning Using
• 984 IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING, VOL. 13, NO. 5, SEPTEMBER 2005 Kernel Eigenvoice Speaker Adaptation
• Journal of Machine Learning Research 6 (2005) 363392 Submitted 12/04; Published 4/05 Core Vector Machines
• Journal of Machine Learning Research xx (200x) xx-xx Submitted 3/2007; Published xx/xx Authors' Reply to the
• IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 15, NO. 6, NOVEMBER 2004 1517 The Pre-Image Problem in Kernel Methods
• Fusing Images with Different Focuses Using Support Vector Machines
• Combination of images with diverse focuses using the spatial frequency
• 1018 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 10, NO. 5, SEPTEMBER 1999 Moderating the Outputs of Support
• Efficient Sparse Modeling with Automatic Feature Grouping Leon Wenliang Zhong wzhong@cse.ust.hk
• Multi-Label Classification on Tree-and DAG-Structured Hierarchies Wei Bi weibi@cse.ust.hk
• Cost-Sensitive Semi-Supervised Support Vector Machine Yu-Feng Li1
• Making Large-Scale Nystrom Approximation Possible Mu Li limu.cn@gmail.com
• Online Multiple Instance Learning with No Regret James T. Kwok2
• Learning the Kernel in Mahalanobis One-Class Support Vector Machines
• Wavelet-Based Feature Extraction for Microarray Data Classification
• A Regularization Framework for Multiple-Instance Learning Pak-Ming Cheung pakming@cs.ust.hk
• Block-Quantized Kernel Matrix for Fast Spectral Embedding Kai Zhang twinsen@cs.ust.hk
• FAST SPEAKER ADAPTION VIA MAXIMUM PENALIZED LIKELIHOOD KERNEL REGRESSION
• Position Estimation for Wireless Sensor Networks K.-F. Simon Wong Ivor W. Tsang Victor Cheung S.-H. Gary Chan James T. Kwok
• Applying Neighborhood Consistency for Fast Clustering and Kernel Density Estimation
• Very Large SVM Training using Core Vector Machines Ivor W. Tsang James T. Kwok
• Bayesian Inference on Principal Component Analysis using Reversible Jump Markov Chain Monte Carlo
• Scaling up Support Vector Data Description by Using Core-Sets
• A STUDY OF VARIOUS COMPOSITE KERNELS FOR KERNEL EIGENVOICE SPEAKER Brian Mak, James T. Kwok, and Simon Ho
• The Pre-Image Problem in Kernel Methods James T. Kwok jamesk@cs.ust.hk
• Distance Metric Learning with Kernels Ivor W. Tsang and James T. Kwok
• Maximum Margin Clustering Made Practical Kai Zhang twinsen@cse.ust.hk
• Automated Text Categorization Using Support Vector Machine
• Mining customer preference ratings for product recommendation using
• Improving DeNoising by Coefficient DeNoising and Dyadic Wavelet Transform Hailong Zhu James T. Kwok
• Unsupervised Maximum Margin Feature Selection with Manifold Regularization , James Kwok
• Proceedings of International Joint Conference on Neural Networks, Montreal, Canada, July 31 -August 4, 2005 0-7803-9048-2/05/$20.00 2005 IEEE
• Improving the Approximation and Convergence Capabilities of Projection Pursuit Learning
• Linear Dependency Between ffl and The Input Noise in fflSupport Vector Regression
• Ecient Hyperkernel Learning Using Second-Order Cone Programming
• Gene Feature Extraction Using T-Test Statistics and Kernel Partial Least Squares
• Proceedings of International Joint Conference on Neural Networks, Montreal, Canada, July 31 -August 4, 2005 0-7803-9048-2/05/$20.00 2005 IEEE
• Ensembles of Partially Trained SVMs with Multiplicative Updates Ivor W. Tsang James T. Kwok
• Incremental Eigen Decomposition James T. Kwok and Haitao Zhao
• IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. XX,NO. YY, MONTH 2003 1 Linear Dependency Between ffl and the Input Noise
• IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 22, NO. 2, FEBRUARY 2011 199 Domain Adaptation via Transfer
• A Convex Method for Locating Regions of Interest with Multi-instance Learning
• An Extended Genetic Rule Induction Algorithm Juliet Juan Liu
• IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 19, NO. 10, OCTOBER 2008 1821 HOD process. Therefore, there is a tradeoff between memory and com-
• Mach Learn (2006) 63: 69101 DOI 10.1007/s10994-006-6130-8
• 1564 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 21, NO. 10, OCTOBER 2010 Incorporating the Loss Function into Discriminative
• Efficient Kernel Feature Extraction for Massive Data Sets Ivor W. Tsang
• Fusing Images with Multiple Focuses using Support Vector Machines
• Parametric Distance Metric Learning with Label Information \Lambda Zhihua Zhang, James T. Kwok and DitYan Yeung
• Marginalized Multi-Instance Kernels James T. Kwok Pak-Ming Cheung
• ARTICLE IN PRESS Pattern Recognition ( )
• Adaptive Localization in a Dynamic WiFi Environment Through Multi-view Sinno Jialin Pan, James T. Kwok, Qiang Yang, and Jeffrey Junfeng Pan
• Applying the Bayesian Evidence Framework to -Support Vector Regression
• IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. XX, NO. X, MONTH 2000 1 The Evidence Framework Applied to
• 644 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 21, NO. 4, APRIL 2010 Simplifying Mixture Models Through
• Eigenvoice Speaker Adaptation via Composite James T. Kwok, Brian Mak and Simon Ho
• Tighter and Convex Maximum Margin Clustering Yu-Feng Li1
• Using the discrete wavelet frame transform to merge Landsat TM and SPOT panchromatic images
• Speedup of Kernel Eigenvoice Speaker Adaptation by Embedded Kernel PCA Brian Mak, Simon Ho, and James T. Kwok
• Core Vector Regression for Very Large Regression Problems Ivor W. Tsang ivor@cs.ust.hk
• Multifocus image fusion using artificial neural networks Shutao Li a,b,*, James T. Kwok b
• Moderating the Outputs of Support Vector Machine Classi ers James Tin-Yau Kwok
• Manifold Regularization for Structured Outputs via the Joint Kernel
• Multiple Kernel Clustering James T. Kwok
• Transferring Localization Models Across Space Sinno Jialin Pan1
• Prototype Vector Machine for Large Scale Semi-Supervised Learning Kai Zhang kai zhang@lbl.gov
• Integrating the Evidence Framework and the Support Vector Machine
• Multidimensional Vector Regression for Accurate and Low-Cost Location Estimation in
• Mining Customer Product Ratings for Personalized Marketing Kwok-Wai Cheung 1 James T. Kwok 2 Martin H. Law 2 Kwok-Ching Tsui 1
• Semi-Supervised Learning Using Label Mean Yu-Feng Li LIYF@LAMDA.NJU.EDU.CN
• Diversified SVM Ensembles for Large Data Sets Ivor W. Tsang1
• Locally Adaptive Classification Piloted by Uncertainty Juan Dai jdai5@ie.cuhk.edu.hk
• Simplifying Mixture Models through Function Approximation
• Spectral and Semidefinite Relaxations of the CLUHSIC Algorithm Wen-Yun Yang
• Efficient Classification of Multi-label and Imbalanced Data Using Min-Max Modular Classifiers
• Accelerated Gradient Methods for Stochastic Optimization and Online Learning
• Domain Adaptation via Transfer Component Analysis Sinno Jialin Pan1
• Proceedings of International Joint Conference on Neural Networks, Montreal, Canada, July 31 -August 4, 2005 0-7803-9048-2/05/$20.00 2005 IEEE
• 610 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 19, NO. 4, APRIL 2008 Large-Scale Maximum Margin Discriminant Analysis
• Bayesian Inference for Transductive Learning of Kernel Matrix Using the Tanner-Wong Data Augmentation Algorithm
• Texture Classification using the Support Vector Machines
• Rival Penalized Competitive Learning for ModelBased Sequence Clustering Martin H. Law
• Simpler Core Vector Machines with Enclosing Balls Ivor W. Tsang IVOR@CSE.UST.HK
• Multimodal Registration using the Discrete Wavelet Frame Transform Jinglin Peng1
• 16 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. XX, NO. Y, MONTH 1999 [107] T.Y. Kwok and D.Y. Yeung, ``Use of bias term in projection
• Support Vector Mixture for Classification and Regression Problems
• Surrogate Maximization/Minimization Algorithms for AdaBoost and the Logistic Regression Model
• Accurate and Low-cost Location Estimation Using Kernels Jeffery Junfeng Pana
• Maximum Margin Clustering with Multivariate Loss Function , James Kwok
• Accelerated Gradient Method for Multi-Task Sparse Learning Problem Xi Chen Weike Pan James T. Kwok Jaime G. Carbonell
• Bayesian Support Vector Regression Martin H. Law (martin@cs.ust.hk) James T. Kwok (jamesk@cs.ust.hk)
• IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 7, NO. 5, SEPTEMBER 1996 1 Use of Bias Term in
• IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 8, NO. 5, SEPTEMBER 1997 1131 Objective Functions for Training New Hidden
• Structured Clustering with Automatic Kernel Adaptation Weike Pan and James T. Kwok
• Time and Space Efficient Spectral Clustering via Column Sampling Xiao-Chen Lian1
• IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS--PART B: CYBERNETICS, VOL. 41, NO. 4, AUGUST 2011 1003 A Hybrid PSO-BFGS Strategy for Global