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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https://doi.org/10.1007/978-1-4419-9326-7Bagging Predictors; Basic Boosting; Ensemble learning; Object Detection; classification algorithm; deep n

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The Sales Sat Nav for Media Consultantsny 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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The Salience of Marketing Stimuliprobability 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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https://doi.org/10.1057/9780230338074 [6], Random Forests are an extension of Breiman’s bagging idea [5] and were developed as a competitor to boosting. Random Forests can be used for either a categorical response variable, referred to in [6] as “classification,” or a continuous response, referred to as “regression.” Similarly, the pre

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https://doi.org/10.1007/b106381ithm which considers the cooperation and interaction among the ensemble members. NCL introduces a correlation penalty term into the cost function of each individual learner so that each learner minimizes its mean-square-error (MSE) error together with the correlation with other ensemble members.

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https://doi.org/10.1007/978-1-4419-5987-4f kernel matrices. The Nystr?m method is a popular technique to generate low-rank matrix approximations but it requires sampling of a large number of columns from the original matrix to achieve good accuracy. This chapter describes a new family of algorithms based on mixtures of Nystr?m approximatio

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