Titlebook: Ensemble Machine Learning; Methods and Applicat Cha Zhang,Yunqian Ma Book 2012 Springer Science+Business Media, LLC 2012 Bagging Predictors

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Ensemble Learning,ms, such as feature selection, confidence estimation, missing feature, incremental learning, error correction, class-imbalanced data, learning concept drift from nonstationary distributions, among others. This chapter provides an overview of ensemble systems, their properties, and how they can be applied to such a wide spectrum of applications.

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Ensemble Learning,elligence and machine learning community. This attention has been well deserved, as ensemble systems have proven themselves to be very effective and extremely versatile in a broad spectrum of problem domains and real-world applications. Originally developed to reduce the variance—thereby improving t

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Boosting Algorithms: A Review of Methods, Theory, and Applications,ny of the simple classifiers alone. A . (WL) is a learning algorithm capable of producing classifiers with probability of error strictly (but only slightly) less than that of random guessing (0.5, in the binary case). On the other hand, a . (SL) is able (given enough training data) to yield classifi

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Targeted Learning,probability distributions .. One refers to . as the statistical model for .. We consider so called semiparametric models that cannot be parameterized by a finite dimensional Euclidean vector. In addition, suppose that our target parameter of interest is a parameter ., so that ψ. = .(.) denotes the p

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