Supervised Learning with Minimal Effort
暂无分享,去创建一个
[1] Philip S. Yu,et al. Text classification without negative examples revisit , 2006, IEEE Transactions on Knowledge and Data Engineering.
[2] Stephen Krashen,et al. The Input Hypothesis: Issues and Implications , 1986 .
[3] David A. Cohn,et al. Active Learning with Statistical Models , 1996, NIPS.
[4] Samuel Williams,et al. A design methodology for domain-optimized power-efficient supercomputing , 2009, Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis.
[5] Andrew McCallum,et al. Piecewise pseudolikelihood for efficient training of conditional random fields , 2007, ICML '07.
[6] Wilfred Pinfold,et al. Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis , 2009, HiPC 2009.
[7] Andrew McCallum,et al. Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data , 2004, J. Mach. Learn. Res..
[8] Peter A. Flach,et al. Delegating classifiers , 2004, ICML.
[9] Peter Stone,et al. Cross-domain transfer for reinforcement learning , 2007, ICML '07.
[10] Miroslav Kubat,et al. Combining Subclassifiers in Text Categorization: A DST-Based Solution and a Case Study , 2007, IEEE Transactions on Knowledge and Data Engineering.
[11] Wu-chun Feng,et al. Towards efficient supercomputing: a quest for the right metric , 2005, 19th IEEE International Parallel and Distributed Processing Symposium.
[12] Dong-lin,et al. Krashen's Input Hypothesis and English classroom teaching , 2008 .
[13] Ran El-Yaniv,et al. Online Choice of Active Learning Algorithms , 2003, J. Mach. Learn. Res..
[14] Marc'Aurelio Ranzato,et al. Sparse Feature Learning for Deep Belief Networks , 2007, NIPS.
[15] Sebastian Thrun,et al. Is Learning The n-th Thing Any Easier Than Learning The First? , 1995, NIPS.
[16] Christophe G. Giraud-Carrier,et al. A Note on the Utility of Incremental Learning , 2000, AI Commun..
[17] John Blitzer,et al. Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification , 2007, ACL.
[18] Paul E. Utgoff,et al. Improved Training Via Incremental Learning , 1989, ML.
[19] Kai A. Krueger,et al. Flexible shaping: How learning in small steps helps , 2009, Cognition.
[20] Yoshua Bengio,et al. Exploring Strategies for Training Deep Neural Networks , 2009, J. Mach. Learn. Res..
[21] Steffen Lange,et al. On the power of incremental learning , 2002, Theor. Comput. Sci..
[22] Jason Weston,et al. Curriculum learning , 2009, ICML '09.
[23] R. Gagne. Conditions of Learning , 1965 .