Semi-Supervised Additive Logistic Regression: A Gradient Descent Solution
暂无分享,去创建一个
Feiping Nie | Yangqiu Song | Changshui Zhang | Qutang Cai | Yangqiu Song | F. Nie | Changshui Zhang | Qutang Cai
[1] Wenxin Jiang,et al. Is regularization unnecessary for boosting? , 2001, AISTATS.
[2] Zoubin Ghahramani,et al. Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions , 2003, ICML 2003.
[3] J. Friedman. Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .
[4] Ji Zhu,et al. Boosting as a Regularized Path to a Maximum Margin Classifier , 2004, J. Mach. Learn. Res..
[5] Lorenzo Rosasco,et al. Manifold Regularization , 2007 .
[6] Ayhan Demiriz,et al. Exploiting unlabeled data in ensemble methods , 2002, KDD.
[7] Xiaojin Zhu,et al. --1 CONTENTS , 2006 .
[8] Eric R. Ziegel,et al. The Elements of Statistical Learning , 2003, Technometrics.
[9] Gunnar Rätsch,et al. Advanced Lectures on Machine Learning , 2004, Lecture Notes in Computer Science.
[10] Bernhard Schölkopf,et al. Learning with Local and Global Consistency , 2003, NIPS.
[11] Balázs Kégl,et al. Boosting on Manifolds: Adaptive Regularization of Base Classifiers , 2004, NIPS.
[12] Christophe Ambroise,et al. Semi-supervised MarginBoost , 2001, NIPS.
[13] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.
[14] Peter L. Bartlett,et al. Functional Gradient Techniques for Combining Hypotheses , 2000 .
[15] Gunnar Rätsch,et al. An Introduction to Boosting and Leveraging , 2002, Machine Learning Summer School.
[16] Gunnar Rätsch,et al. Soft Margins for AdaBoost , 2001, Machine Learning.
[17] Mikhail Belkin,et al. Towards a theoretical foundation for Laplacian-based manifold methods , 2005, J. Comput. Syst. Sci..
[18] Alexander J. Smola,et al. Advances in Large Margin Classifiers , 2000 .