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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Online Bayesian Learning With Natural Sequential Prior Distribution
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Online Bayesian Learning With Natural Sequential Prior Distribution

机译：具有自然顺序先验分布的在线贝叶斯学习

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摘要

Online Bayesian learning has been successfully applied to online learning for multilayer perceptrons and radial basis functions. In online Bayesian learning, typically, the conventional transition model has been used. Although the conventional transition model is based on the squared norm of the difference between the current parameter vector and the previous parameter vector, the transition model does not adequately consider the difference between the current observation model and the previous observation model. To adequately consider this difference between the observation models, we propose a natural sequential prior. The proposed transition model uses a Fisher information matrix to consider the difference between the observation models more naturally. For validation, the proposed transition model is applied to an online learning problem for a three-layer perceptron.
机译：在线贝叶斯学习已成功应用于多层感知器和径向基函数的在线学习。通常，在在线贝叶斯学习中，使用了传统的过渡模型。尽管传统的过渡模型基于当前参数向量和先前参数向量之间的差的平方范数，但是过渡模型并未充分考虑当前观察模型和先前观察模型之间的差。为了充分考虑观察模型之间的这种差异，我们提出了自然顺序先验。提出的过渡模型使用Fisher信息矩阵来更自然地考虑观察模型之间的差异。为了进行验证，将提出的过渡模型应用于三层感知器的在线学习问题。

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