Support vector machine with hypergraph-based pairwise constraints
暂无分享,去创建一个
Ling Jing | Ling Zhen | Qiuling Hou | Meng Lv | Ling Jing | Meng Lv | Ling Zhen | Qiuling Hou
[1] Aiguo Song,et al. Improving clustering with pairwise constraints: a discriminative approach , 2012, Knowledge and Information Systems.
[2] Charu C. Aggarwal,et al. Towards graphical models for text processing , 2012, Knowledge and Information Systems.
[3] Johan A. K. Suykens,et al. Least squares support vector machine classifiers: a large scale algorithm , 1999 .
[4] Ming Cheng,et al. Combinative hypergraph learning for semi-supervised image classification , 2015, Neurocomputing.
[5] David G. Stork,et al. Pattern Classification (2nd ed.) , 1999 .
[6] Luiz Eduardo Soares de Oliveira,et al. Pairwise fusion matrix for combining classifiers , 2007, Pattern Recognit..
[7] Jue Wang,et al. A general soft method for learning SVM classifiers with L1-norm penalty , 2008, Pattern Recognit..
[8] Philippe Rigollet,et al. Generalization Error Bounds in Semi-supervised Classification Under the Cluster Assumption , 2006, J. Mach. Learn. Res..
[9] Changshui Zhang,et al. Boosting with pairwise constraints , 2010, Neurocomputing.
[10] Michael I. Jordan,et al. A Robust Minimax Approach to Classification , 2003, J. Mach. Learn. Res..
[11] Min Wu,et al. Multi-label ensemble based on variable pairwise constraint projection , 2013, Inf. Sci..
[12] Thomas G. Dietterich. Adaptive computation and machine learning , 1998 .
[13] Daniel S. Yeung,et al. Structured large margin machines: sensitive to data distributions , 2007, Machine Learning.
[14] Bernhard Schölkopf,et al. Learning with Hypergraphs: Clustering, Classification, and Embedding , 2006, NIPS.
[15] Václav Hlavác,et al. An iterative algorithm learning the maximal margin classifier , 2003, Pattern Recognit..
[16] Nello Cristianini,et al. Kernel Methods for Pattern Analysis , 2003, ICTAI.
[17] S. Sathiya Keerthi,et al. Improvements to Platt's SMO Algorithm for SVM Classifier Design , 2001, Neural Computation.
[18] Anthony Widjaja,et al. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2003, IEEE Transactions on Neural Networks.
[19] Daoqiang Zhang,et al. Bagging Constraint Score for feature selection with pairwise constraints , 2010, Pattern Recognit..
[20] Qiang Yang,et al. Structural Regularized Support Vector Machine: A Framework for Structural Large Margin Classifier , 2011, IEEE Transactions on Neural Networks.
[21] Xinbo Gao,et al. Semi-supervised Gaussian process latent variable model with pairwise constraints , 2010, Neurocomputing.
[22] Ming Yang,et al. A novel hypothesis-margin based approach for feature selection with side pairwise constraints , 2010, Neurocomputing.
[23] Olvi L. Mangasarian,et al. Multisurface proximal support vector machine classification via generalized eigenvalues , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[24] David G. Stork,et al. Pattern Classification , 1973 .
[25] Jun Yu,et al. Pairwise constraints based multiview features fusion for scene classification , 2013, Pattern Recognit..
[26] Meng Wang,et al. Adaptive Hypergraph Learning and its Application in Image Classification , 2012, IEEE Transactions on Image Processing.
[27] TaoDacheng,et al. Semi-supervised Gaussian process latent variable model with pairwise constraints , 2010 .
[28] Marco Maggini,et al. Learning from pairwise constraints by Similarity Neural Networks , 2012, Neural Networks.
[29] Stephen J. Wright,et al. Dissimilarity in Graph-Based Semi-Supervised Classification , 2007, AISTATS.
[30] Qiang Qian,et al. Multi-view classification with cross-view must-link and cannot-link side information , 2013, Knowl. Based Syst..
[31] Zhe Wang,et al. Matrixized learning machine with modified pairwise constraints , 2015, Pattern Recognit..
[32] Joachim M. Buhmann,et al. Learning with constrained and unlabelled data , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[33] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[34] Sergios Theodoridis,et al. A geometric approach to Support Vector Machine (SVM) classification , 2006, IEEE Transactions on Neural Networks.
[35] Mikhail Belkin,et al. Manifold Regularization : A Geometric Framework for Learning from Examples , 2004 .
[36] Federico Girosi,et al. Support Vector Machines: Training and Applications , 1997 .
[37] Nenghai Yu,et al. Maximum Margin Clustering with Pairwise Constraints , 2008, 2008 Eighth IEEE International Conference on Data Mining.
[38] Michael R. Lyu,et al. Learning large margin classifiers locally and globally , 2004, ICML.
[39] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[40] Bernhard Schölkopf,et al. New Support Vector Algorithms , 2000, Neural Computation.