随机学习机的预测误差

Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94) Pub Date : 1994-12-01 DOI:10.1109/ICNN.1994.374346

K. Ikeda, Noboru Murata, S. Amari

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引用次数: 1

摘要

包含的训练样本数量越多，学习机器的表现就越好。了解行为改善的速度和程度是很重要的。平均预测误差是评价行为最常用的标准之一。我们从参数估计的角度来看待机器学习，并利用信息几何方法推导了随机二分类机的平均预测误差

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Prediction error of stochastic learning machine

The more the number of training examples included, the better a learning machine will behave. It is an important to know how fast and how well the behavior is improved. The average prediction error is one of the most popular criteria to evaluate the behavior. We have regarded the machine learning from the point of view of parameter estimation and derived the average prediction error of stochastic dichotomy machines by the information geometrical method.<>

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Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)

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