Modular neural networks for MAP classification of time series and the partition algorithm

Petridis V.; Kehagias A.

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Modular neural networks for MAP classification of time series and the partition algorithm

机译：时间序列MAP分类的模块化神经网络和分区算法

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We apply the partition algorithm to the problem of time-series classification. We assume that the source that generates the time series belongs to a finite set of candidate sources. Classification is based on the computation of posterior probabilities. Prediction error is used to adaptively update the posterior probability of each source. The algorithm is implemented by a hierarchical, modular, recurrent network. The bottom (partition) level of the network consists of neural modules, each one trained to predict the output of one candidate source. The top (decision) level consists of a decision module, which computes posterior probabilities and classifies the time series to the source of maximum posterior probability. The classifier network is formed from the composition of the partition and decision levels. This method applies to deterministic as well as probabilistic time series. Source switching can also be accommodated. We give some examples of application to problems of signal detection, phoneme, and enzyme classification. In conclusion, the algorithm presented here gives a systematic method for the design of modular classification networks. The method can be extended by various choices of the partition and decision components.

机译：我们将划分算法应用于时间序列分类问题。我们假设生成时间序列的源属于一组有限的候选源。分类基于后验概率的计算。预测误差用于自适应地更新每个源的后验概率。该算法由分层的模块化递归网络实现。网络的底层（分区）由神经模块组成，每个神经模块都经过训练以预测一个候选源的输出。最高（决策）级别由决策模块组成，该模块计算后验概率并将时间序列分类为最大后验概率的来源。分类器网络由分区和决策级别的组成组成。此方法适用于确定性和概率时间序列。也可以进行信号源切换。我们举例说明信号检测，音素和酶分类问题。总之，这里提出的算法为模块化分类网络的设计提供了一种系统的方法。可以通过分区和决策组件的各种选择来扩展该方法。

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《IEEE Transactions on Neural Networks》 |1996年第1期|P.73-86|共14页
作者
Petridis V.; Kehagias A.;
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正文语种 eng
中图分类自动化技术、计算机技术;
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Modular neural networks for MAP classification of time series and the partition algorithm

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