Analysis of Dynamical Recognizers
Analysis of Dynamical Recognizers
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动态识别器分析
DOI：
10.1162/neco.1997.9.5.1127
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发表时间：
1997
影响因子：
2.9
通讯作者：
J. Pollack
中科院分区：
文献类型：
--
作者：
A. Blair;J. Pollack
Pollack（1991）证明，二阶循环神经网络可以作为形式语言的动态识别器，在正例和负例上进行训练，并观察到学习过程中的相变和相互作用的函数系统样分形状态集。接下来的工作主要集中在从训练网络中提取和最小化有限状态自动机（FSA）。然而，这样的网络能够归纳出不规则的语言，因此不等同于任何FSA。事实上，对于一个小型网络来说，通过引入这种非规则语言来拟合训练数据可能会更简单。但是什么时候网络语言不正常呢？在本文中，我们使用一个能够学习所有Tomita数据集的低维网络，提出了一种经验方法来测试由网络诱导的语言是否规则。我们还提供了一个详细的“机器分析”训练网络的规则和非规则语言。
Pollack (1991) demonstrated that second-order recurrent neural networks can act as dynamical recognizers for formal languages when trained on positive and negative examples, and observed both phase transitions in learning and interacted function system-like fractal state sets. Follow on work focused mainly on the extraction and minimization of a finite state automaton (FSA) from the trained network. However, such networks are capable of inducing languages that are not regular and therefore not equivalent to any FSA. Indeed, it may be simpler for a small network to fit its training data by inducing such a nonregular language. But when is the network's language not regular? In this article, using a low-dimensional network capable of learning all the Tomita data sets, we present an empirical method for testing whether the language induced by the network is regular. We also provide a detailed "-machine analysis of trained networks for both regular and nonregular languages.