Large Scale Transductive SVMs
暂无分享,去创建一个
Jason Weston | Léon Bottou | Ronan Collobert | Fabian H. Sinz | J. Weston | L. Bottou | Ronan Collobert | Fabian H Sinz | R. Collobert
[1] Bernhard E. Boser,et al. A training algorithm for optimal margin classifiers , 1992, COLT '92.
[2] L. Thi,et al. Analyse numérique des algorithmes de l'optimisation D. C. . Approches locale et globale. Codes et simulations numériques en grande dimension. Applications , 1994 .
[3] Bernhard Schölkopf,et al. Kernel Principal Component Analysis , 1997, ICANN.
[4] Ayhan Demiriz,et al. Semi-Supervised Support Vector Machines , 1998, NIPS.
[5] Thorsten Joachims,et al. Making large-scale support vector machine learning practical , 1999 .
[6] John C. Platt,et al. Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .
[7] Thorsten Joachims,et al. Transductive Inference for Text Classification using Support Vector Machines , 1999, ICML.
[8] Klaus Obermayer,et al. Bayesian Transduction , 1999, NIPS.
[9] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[10] Tommi S. Jaakkola,et al. Partially labeled classification with Markov random walks , 2001, NIPS.
[11] Alan L. Yuille,et al. The Concave-Convex Procedure (CCCP) , 2001, NIPS.
[12] O. Mangasarian,et al. Semi-Supervised Support Vector Machines for Unlabeled Data Classification , 2001 .
[13] Mikhail Belkin,et al. Using manifold structure for partially labelled classification , 2002, NIPS 2002.
[14] Bernhard Schölkopf,et al. Cluster Kernels for Semi-Supervised Learning , 2002, NIPS.
[15] Zoubin Ghahramani,et al. Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions , 2003, ICML 2003.
[16] Nello Cristianini,et al. Convex Methods for Transduction , 2003, NIPS.
[17] Bernhard Schölkopf,et al. Learning with Local and Global Consistency , 2003, NIPS.
[18] Jason Weston,et al. Semi-supervised Protein Classification Using Cluster Kernels , 2003, NIPS.
[19] W. Wong,et al. On ψ-Learning , 2003 .
[20] Chih-Jen Lin,et al. Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel , 2003, Neural Computation.
[21] Yiming Yang,et al. RCV1: A New Benchmark Collection for Text Categorization Research , 2004, J. Mach. Learn. Res..
[22] Dale Schuurmans,et al. Maximum Margin Clustering , 2004, NIPS.
[23] Neil D. Lawrence,et al. Semi-supervised Learning via Gaussian Processes , 2004, NIPS.
[24] Yoram Singer,et al. Leveraging the margin more carefully , 2004, ICML.
[25] Peter L. Bartlett,et al. Improved Generalization Through Explicit Optimization of Margins , 2000, Machine Learning.
[26] Ran El-Yaniv,et al. Explicit Learning Curves for Transduction and Application to Clustering and Compression Algorithms , 2004, J. Artif. Intell. Res..
[27] Jason Weston,et al. Fast Kernel Classifiers with Online and Active Learning , 2005, J. Mach. Learn. Res..
[28] Thomas Hofmann,et al. Kernel Methods for Missing Variables , 2005, AISTATS.
[29] S. Sathiya Keerthi,et al. A Modified Finite Newton Method for Fast Solution of Large Scale Linear SVMs , 2005, J. Mach. Learn. Res..
[30] Alexander Zien,et al. Semi-Supervised Classification by Low Density Separation , 2005, AISTATS.
[31] Mikhail Belkin,et al. Beyond the point cloud: from transductive to semi-supervised learning , 2005, ICML.
[32] Jason Weston,et al. Trading convexity for scalability , 2006, ICML.
[33] V. Vapnik. Estimation of Dependences Based on Empirical Data , 2006 .