Competing with Wild Prediction Rules
暂无分享,去创建一个
[1] Ingo Steinwart,et al. On the Influence of the Kernel on the Consistency of Support Vector Machines , 2002, J. Mach. Learn. Res..
[2] Mikio Kato,et al. Clarkson and Random Clarkson Inequalities for Lr(X) , 1997 .
[4] Vladimir Vovk. Defensive Prediction with Expert Advice , 2005, ALT.
[5] Vladimir Vovk,et al. Competing with wild prediction rules , 2005, Machine Learning.
[6] H. Sorenson. Least-squares estimation: from Gauss to Kalman , 1970, IEEE Spectrum.
[7] J. A. Clarkson. Uniformly convex spaces , 1936 .
[8] Göte Nordlander. The modulus of convexity in normed linear spaces , 1960 .
[9] Claudio Gentile,et al. Adaptive and Self-Confident On-Line Learning Algorithms , 2000, J. Comput. Syst. Sci..
[10] William H. Press,et al. Numerical Recipes in C, 2nd Edition , 1992 .
[11] Józef Banaś,et al. Deformation of Banach spaces , 1993 .
[12] T. Figiel. On the moduli of convexity and smoothness , 1976 .
[13] S. M. Nikol'skii,et al. ON IMBEDDING, CONTINUATION AND APPROXIMATION THEOREMS FOR DIFFERENTIABLE FUNCTIONS OF SEVERAL VARIABLES , 1961 .
[14] A. Kolmogoroff. Grundbegriffe der Wahrscheinlichkeitsrechnung , 1933 .
[15] J. T. Marti. Evaluation of the Least Constant in Sobolev’s Inequality for $H^1 (0,s)$ , 1983 .
[16] Vladimir Vovk,et al. Metric entropy in competitive on-line prediction , 2006, ArXiv.
[17] O. Hanner. On the uniform convexity ofLp andlp , 1956 .
[18] William H. Press,et al. Numerical recipes in C , 2002 .
[19] Vladimir Vovk,et al. On-Line Regression Competitive with Reproducing Kernel Hilbert Spaces , 2005, TAMC.
[20] Philip M. Long,et al. WORST-CASE QUADRATIC LOSS BOUNDS FOR ON-LINE PREDICTION OF LINEAR FUNCTIONS BY GRADIENT DESCENT , 1993 .
[21] Diomedes Barcenas,et al. On Moduli of Convexity in Banach Spaces , 2004 .
[22] G. Shafer,et al. Probability and Finance: It's Only a Game! , 2001 .
[23] Manfred K. Warmuth,et al. Exponentiated Gradient Versus Gradient Descent for Linear Predictors , 1997, Inf. Comput..
[24] Ioannis Karatzas,et al. Brownian Motion and Stochastic Calculus , 1987 .
[25] W. Hoeffding. Probability Inequalities for sums of Bounded Random Variables , 1963 .
[26] H. Vincent Poor,et al. Linear estimation of self-similar processes via Lamperti's transformation , 2000 .
[27] R. E. Kalman,et al. New Results in Linear Filtering and Prediction Theory , 1961 .
[28] N. Aronszajn. Theory of Reproducing Kernels. , 1950 .
[29] Joram Lindenstrauss. On the modulus of smoothness and divergent series in Banach spaces. , 1963 .
[30] Claudio Gentile,et al. On the generalization ability of on-line learning algorithms , 2001, IEEE Transactions on Information Theory.
[31] Vladimir Vovk. Competitive on-line learning with a convex loss function , 2005, ArXiv.
[32] Philip M. Long,et al. Worst-case quadratic loss bounds for prediction using linear functions and gradient descent , 1996, IEEE Trans. Neural Networks.
[33] R. E. Kalman,et al. A New Approach to Linear Filtering and Prediction Problems , 2002 .