贝叶斯在线学习:具有rao - blackwell化的顺序蒙特卡罗

Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing Pub Date : 2002-11-07 DOI:10.1109/NNSP.2002.1030021

K. Yosui, T. Kurihara, K. Wada, T. Souma, Takashi Matsumoto

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引用次数: 10

摘要

提出了一种用于前馈神经网络在线学习的rao - blackwell化序贯蒙特卡罗(RBSMC)方案。通过实例验证了该算法的性能，并与传统的时序蒙特卡罗滤波和扩展卡尔曼滤波(EKF)进行了比较。该方案优于传统算法。

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Bayesian on-line learning: a sequential Monte Carlo with Rao-Blackwellization

This paper proposes a Rao-Blackwellised sequential Monte Carlo (RBSMC) scheme for on-line learning with feedforward neural nets. The proposed algorithm is tested against an example and the performance is compared with those of the conventional sequential Monte Carlo as well as the extended Kalman filter (EKF). The proposed scheme outperforms those conventional algorithms.

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Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing

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