Improving Generalization Ability through Active Learning
Improving Generalization Ability through Active Learning
复制标题
通过主动学习提高泛化能力
DOI：
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发表时间：
1999
期刊：
影响因子：
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通讯作者：
H. Ogawa
中科院分区：
文献类型：
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作者：
S. Vijayakumar;H. Ogawa
本文讨论了提高神经网络泛化能力的主动训练数据选择问题。我们从函数逼近的角度来看待学习问题，并将其形式化为逆问题。基于这个框架，我们解析地推导出了一种选择相对于Wiener优化准则优化的训练数据集的方法。最终结果使用原始函数系综上的先验相关信息来设计有效的采样方案，当与这里描述的学习方案结合使用时，该采样方案被展示为导致最佳泛化。通过一个仿真算例和一个高维函数空间的学习问题验证了这一结果。关键词：主动学习、维纳优化准则、泛化、反问题、训练数据选择
In this paper, we discuss the problem of active training data selection for improving the generalization capability of a neural network. We look at the learning problem from a function approximation perspective and formalize it as an inverse problem. Based on this framework, we analytically derive a method of choosing a training data set optimized with respect to the Wiener optimization criterion. The final result uses the apriori correlation information on the original function ensemble to devise an efficient sampling scheme which, when used in conjunction with the learning scheme described here, is shown to result in optimal generalization. This result is substantiated through a simulated example and a learning problem in high dimensional function space. key words: active learning, wiener optimization criterion, generalization, inverse problem, training data selection