Active Selection of Training Examples for Network Learning

该项目涉及的技术，积极选择 用于神经网络学习的训练示例， 同时扩大网络以适应数据。 的方法 使用统计抽样标准，积分均方 错误，推导出一个“贪婪”的选择标准， 下一个训练示例，最大化此 measure. 这种选择标准适用于广泛的类别 估计器。 该方案的一个实际实现， 层神经网络。 //

This project is concerned with techniques for active selection of training examples for neural network learning, while simultaneously growing the network to fit the data. The approach uses a statistical sampling criterion, Integrated Mean Squared Error, to derive a "greedy" selection criterion which picks the next training example that maximizes the decrement in this measure. This selection criterion is usable for a wide class estimators. A practical realization of this schemes for multi- layer neural networks is demonstrated. //