Bias vs Variance Decomposition for Regression and Classification
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[1] Pierre Geurts,et al. Contributions to decision tree induction: bias/variance tradeoff and time series classification , 2002 .
[2] Eric Bauer,et al. An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants , 1999, Machine Learning.
[3] Christopher M. Bishop,et al. Neural networks for pattern recognition , 1995 .
[4] Geoffrey I. Webb. MultiBoosting: A Technique for Combining Boosting and Wagging , 2000, Machine Learning.
[5] Gareth James,et al. Variance and Bias for General Loss Functions , 2003, Machine Learning.
[6] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.
[7] Jerome H. Friedman,et al. On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality , 2004, Data Mining and Knowledge Discovery.
[8] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[9] S. T. Buckland,et al. An Introduction to the Bootstrap , 1994 .
[10] Leo Breiman,et al. Classification and Regression Trees , 1984 .
[11] Elie Bienenstock,et al. Neural Networks and the Bias/Variance Dilemma , 1992, Neural Computation.
[12] David H. Wolpert,et al. On Bias Plus Variance , 1997, Neural Computation.
[13] Wei-Yin Loh,et al. Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..
[14] Tom Heskes,et al. Bias/Variance Decompositions for Likelihood-Based Estimators , 1998, Neural Computation.
[15] Leo Breiman,et al. Randomizing Outputs to Increase Prediction Accuracy , 2000, Machine Learning.
[16] Ron Kohavi,et al. Bias Plus Variance Decomposition for Zero-One Loss Functions , 1996, ICML.