Bias-Variance Trade-Offs: Novel Applications
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[1] David H. Wolpert,et al. Distributed Constrained Optimization with Semicoordinate Transformations , 2008, ArXiv.
[2] David H. Wolpert,et al. Parametric Learning and Monte Carlo Optimization , 2007, ArXiv.
[3] David H. Wolpert,et al. Advances in Distributed Optimization Using Probability Collectives , 2006, Adv. Complex Syst..
[4] V. Vapnik. Estimation of Dependences Based on Empirical Data , 2006 .
[5] David H. Wolpert,et al. Distributed control by Lagrangian steepest descent , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).
[6] Hoon Kim,et al. Monte Carlo Statistical Methods , 2000, Technometrics.
[7] Padhraic Smyth,et al. Linearly Combining Density Estimators via Stacking , 1999, Machine Learning.
[8] Yuri Ermoliev,et al. Monte Carlo Optimization and Path Dependent Nonstationary Laws of Large Numbers , 1998 .
[9] David H. Wolpert,et al. On Bias Plus Variance , 1997, Neural Computation.
[10] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[11] L. Breiman. Stacked Regressions , 1996, Machine Learning.
[12] Dana Angluin,et al. Computational learning theory: survey and selected bibliography , 1992, STOC '92.
[13] J. Berger. Statistical Decision Theory and Bayesian Analysis , 1988 .
[14] G. Lepage. A new algorithm for adaptive multidimensional integration , 1978 .
[15] Dirk P. Kroese,et al. The Cross‐Entropy Method , 2004 .
[16] David J. C. MacKay,et al. Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.
[17] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[18] Vladimir Vapnik,et al. The Nature of Statistical Learning , 1995 .
[19] Wray L. Buntine,et al. Bayesian Back-Propagation , 1991, Complex Syst..