Boosting Multi-Objective Regression Trees

机译：提高多目标回归树

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Boosting was introduced to improve weak classifiers but it has also been shown to be a powerful learning tool applicable to a wide variety of situations and methods, including regression trees. Using a very general mixture approach to motivate splitting rules for regression trees, a boosting algorithm is developed for regression problems with multivariate response vectors. Details of the algorithm will be discussed as well as an example based on predicting cholesterol and triglyceride levels.

机译：引入提升以改善弱分类器，但它也被证明是一种强大的学习工具，适用于各种情况和方法，包括回归树。使用非常一般的混合方法来激励回归树的分裂规则，开发了一种升压算法，用于多变量响应向量的回归问题。将讨论算法的细节，以及基于预测胆固醇和甘油三酯水平的示例。

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《Symposium on the interface》|2002年||共9页
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中图分类计算技术、计算机技术;
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4. Boosting Multi-Objective Regression Trees [C] . Symposium on the interface . 2002

机译：提高多目标回归树
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Boosting Multi-Objective Regression Trees

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