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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Application of Reinforcement Learning Algorithms for the Adaptive Computation of the Smoothing Parameter for Probabilistic Neural Network
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Application of Reinforcement Learning Algorithms for the Adaptive Computation of the Smoothing Parameter for Probabilistic Neural Network

机译：强化学习算法在概率神经网络平滑参数自适应计算中的应用

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摘要

In this paper, we propose new methods for the choice and adaptation of the smoothing parameter of the probabilistic neural network (PNN). These methods are based on three reinforcement learning algorithms: -learning, -learning, and stateless -learning. We regard three types of PNN classifiers: the model that uses single smoothing parameter for the whole network, the model that utilizes single smoothing parameter for each data attribute, and the model that possesses the matrix of smoothing parameters different for each data variable and data class. Reinforcement learning is applied as the method of finding such a value of the smoothing parameter, which ensures the maximization of the prediction ability. PNN models with smoothing parameters computed according to the proposed algorithms are tested on eight databases by calculating the test error with the use of the cross validation procedure. The results are compared with state-of-the-art methods for PNN training published in the literature up to date and, additionally, with PNN whose sigma is determined by means of the conjugate gradient approach. The results demonstrate that the proposed approaches can be used as alternative PNN training procedures.
机译：在本文中，我们提出了新的方法来选择和调整概率神经网络（PNN）的平滑参数。这些方法基于三种强化学习算法：-learning，-learning和无状态-learning。我们考虑三种类型的PNN分类器：对整个网络使用单个平滑参数的模型，对每个数据属性使用单个平滑参数的模型，以及对每个数据变量和数据类都具有不同的平滑参数矩阵的模型。强化学习被用作找到这样的平滑参数值的方法，这确保了预测能力的最大化。通过使用交叉验证程序计算测试误差，在八个数据库上测试了具有根据所提出算法计算出的平滑参数的PNN模型。将结果与最新文献中最新的PNN训练方法进行比较，此外，将其与通过共轭梯度法确定sigma的PNN进行比较。结果表明，所提出的方法可以用作替代的PNN训练程序。

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