A Universal Well-Calibrated Algorithm for On-line Classification
暂无分享,去创建一个
[1] Alexander Gammerman,et al. Machine-Learning Applications of Algorithmic Randomness , 1999, ICML.
[2] Vladimir Vovk,et al. On-line confidence machines are well-calibrated , 2002, The 43rd Annual IEEE Symposium on Foundations of Computer Science, 2002. Proceedings..
[3] Vladimir Vovk,et al. Well-calibrated predictions from on-line compression models , 2006, Theor. Comput. Sci..
[4] Y. Mansour,et al. Generalization bounds for averaged classifiers , 2004, math/0410092.
[5] Clifford Stein,et al. Introduction to Algorithms, 2nd edition. , 2001 .
[6] Ronald L. Rivest,et al. Learning complicated concepts reliably and usefully , 1988, Annual Conference Computational Learning Theory.
[7] Vladimir Vovk,et al. Online Region Prediction with Real Teachers , 2003 .
[8] Vladimir Vovk,et al. Asymptotic Optimality of Transductive Confidence Machine , 2002, ALT.
[9] M. Kendall. Theoretical Statistics , 1956, Nature.
[10] Alexander Gammerman,et al. Testing Exchangeability On-Line , 2003, ICML.
[11] G. Lugosi,et al. On the Strong Universal Consistency of Nearest Neighbor Regression Function Estimates , 1994 .
[12] Vladimir Vovk,et al. Criterion of calibration for transductive confidence machine with limited feedback , 2006, Theor. Comput. Sci..
[13] László Györfi,et al. A Probabilistic Theory of Pattern Recognition , 1996, Stochastic Modelling and Applied Probability.
[14] Alexander Gammerman,et al. Transduction with Confidence and Credibility , 1999, IJCAI.
[15] A. Shiryaev,et al. Probability (2nd ed.) , 1995, Technometrics.
[16] 中澤 真,et al. Devroye, L., Gyorfi, L. and Lugosi, G. : A Probabilistic Theory of Pattern Recognition, Springer (1996). , 1997 .