Kernel learning and optimization with Hilbert–Schmidt independence criterion

[1]  Haitao Xu,et al.  Multiple rank multi-linear kernel support vector machine for matrix data classification , 2018, Int. J. Mach. Learn. Cybern..

[2]  Peng Liu,et al.  Two-stage extreme learning machine for high-dimensional data , 2016, Int. J. Mach. Learn. Cybern..

[3]  Binbin Pan,et al.  A Novel Framework for Learning Geometry-Aware Kernels , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[4]  Liang Tao,et al.  Learning shared subspace for multi-label dimensionality reduction via dependence maximization , 2015, Neurocomputing.

[5]  Dongyan Zhao,et al.  An overview of kernel alignment and its applications , 2015, Artificial Intelligence Review.

[6]  Jing-Yu Yang,et al.  Multiple kernel clustering based on centered kernel alignment , 2014, Pattern Recognit..

[7]  Dongyan Zhao,et al.  Two-stage multiple kernel learning with multiclass kernel polarization , 2013, Knowl. Based Syst..

[8]  Masashi Sugiyama,et al.  On Kernel Parameter Selection in Hilbert-Schmidt Independence Criterion , 2012, IEICE Trans. Inf. Syst..

[9]  Zhi-Hua Zhou,et al.  Non-Parametric Kernel Learning with robust pairwise constraints , 2012, Int. J. Mach. Learn. Cybern..

[10]  Mehryar Mohri,et al.  Algorithms for Learning Kernels Based on Centered Alignment , 2012, J. Mach. Learn. Res..

[11]  Bernhard Schölkopf,et al.  A Kernel Two-Sample Test , 2012, J. Mach. Learn. Res..

[12]  Masashi Sugiyama,et al.  High-Dimensional Feature Selection by Feature-Wise Kernelized Lasso , 2012, Neural Computation.

[13]  Yong Liu,et al.  Learning kernels with upper bounds of leave-one-out error , 2011, CIKM '11.

[14]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[15]  Ethem Alpaydin,et al.  Multiple Kernel Learning Algorithms , 2011, J. Mach. Learn. Res..

[16]  Ivor W. Tsang,et al.  Incorporating the Loss Function Into Discriminative Clustering of Structured Outputs , 2010, IEEE Transactions on Neural Networks.

[17]  Bernhard Schölkopf,et al.  Remote Sensing Feature Selection by Kernel Dependence Measures , 2010, IEEE Geoscience and Remote Sensing Letters.

[18]  Houkuan Huang,et al.  Learning by local kernel polarization , 2009, Neurocomputing.

[19]  Tu Bao Ho,et al.  An efficient kernel matrix evaluation measure , 2008, Pattern Recognit..

[20]  Lei Wang,et al.  Feature Selection with Kernel Class Separability , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Jieping Ye,et al.  Learning subspace kernels for classification , 2008, KDD.

[22]  Bernhard Schölkopf,et al.  Kernel Measures of Conditional Dependence , 2007, NIPS.

[23]  Le Song,et al.  A Kernel Statistical Test of Independence , 2007, NIPS.

[24]  Le Song,et al.  A dependence maximization view of clustering , 2007, ICML '07.

[25]  J. Demšar.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[26]  Bernhard Schölkopf,et al.  Measuring Statistical Dependence with Hilbert-Schmidt Norms , 2005, ALT.

[27]  Yoram Baram,et al.  Learning by Kernel Polarization , 2005, Neural Computation.

[28]  Chih-Jen Lin,et al.  A tutorial on?-support vector machines , 2005 .

[29]  Michael I. Jordan,et al.  Kernel independent component analysis , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[30]  Simon Haykin,et al.  On Different Facets of Regularization Theory , 2002, Neural Computation.

[31]  S. Sathiya Keerthi,et al.  Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms , 2002, IEEE Trans. Neural Networks.

[32]  Sayan Mukherjee,et al.  Choosing Multiple Parameters for Support Vector Machines , 2002, Machine Learning.

[33]  Sheng-De Wang,et al.  Fuzzy support vector machines , 2002, IEEE Trans. Neural Networks.

[34]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[35]  N. Cristianini,et al.  On Kernel-Target Alignment , 2001, NIPS.

[36]  Le Song,et al.  Feature Selection via Dependence Maximization , 2012, J. Mach. Learn. Res..

[37]  Bernhard Schölkopf,et al.  Kernel Constrained Covariance for Dependence Measurement , 2005, AISTATS.

[38]  Nello Cristianini,et al.  Kernel Methods for Pattern Analysis , 2004 .