Neural Network Ensembles, Cross Validation, and Active Learning
Neural Network Ensembles, Cross Validation, and Active Learning
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发表时间：
1994
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通讯作者：
A. Krogh;Jesper Vedelsby
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作者：
A. Krogh;Jesper Vedelsby
使用神经网络集成（委员会）学习连续值函数能够提高准确性、可靠地估计泛化误差以及实现主动学习。模糊度被定义为集成成员在未标记数据上的平均输出的变化，因此它量化了网络之间的不一致性。讨论了如何结合交叉验证使用模糊度来可靠地估计集成泛化误差，以及这种类型的集成交叉验证如何有时能提高性能。展示了如何使用未标记数据估计集成成员的最优权重。通过对委员会查询的推广，最后展示了如何在主动学习方案中使用模糊度来选择要标记的新训练数据。
Learning of continuous valued functions using neural network ensembles (committees) can give improved accuracy, reliable estimation of the generalization error, and active learning. The ambiguity is defined as the variation of the output of ensemble members averaged over unlabeled data, so it quantifies the disagreement among the networks. It is discussed how to use the ambiguity in combination with cross-validation to give a reliable estimate of the ensemble generalization error, and how this type of ensemble cross-validation can sometimes improve performance. It is shown how to estimate the optimal weights of the ensemble members using unlabeled data. By a generalization of query by committee, it is finally shown how the ambiguity can be used to select new training data to be labeled in an active learning scheme.