消除神经网络集成中的多重共线性问题：增量、负相关、最优凸混合
Eliminating Multicollinearity Issues in Neural Network Ensembles: Incremental, Negatively Correlated, Optimal Convex Blending
摘要：
给定一个{features，target}数据集，我们引入了一个增量算法，该算法使用神经网络的集成来构建一个聚合回归量。众所周知，集成方法遭受多重共线性问题，这是主要由于公共训练数据集而产生的冗余的表现。在目前的增量方法中，在每个阶段，我们最佳地混合的总回归与新训练的神经网络下的凸性约束，如果必要的话，诱导负相关。在此框架下，共线性问题根本不会出现，因此该方法既准确又稳健。
英文摘要：
Given a {features, target} dataset, we introduce an incremental algorithm that constructs an aggregate regressor, using an ensemble of neural networks. It is well known that ensemble methods suffer from the multicollinearity issue, which is the manifestation of redundancy arising mainly due to the common training-dataset. In the present incremental approach, at each stage we optimally blend the aggregate regressor with a newly trained neural network under a convexity constraint which, if necessary, induces negative correlations. Under this framework, collinearity issues do not arise at all, rendering so the method both accurate and robust.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
