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首页> 外文期刊>Connection Science >Greedy information acquisition algorithm: a new information theoretic approach to dynamic information acquisition in neural networks
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Greedy information acquisition algorithm: a new information theoretic approach to dynamic information acquisition in neural networks

机译：贪婪信息获取算法：神经网络中动态信息获取的一种新的信息理论方法

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In this paper, we propose a new information theoretic approach to competitive learning. The new approach is called greedy information acquisition, because networks try to absorb as much information as possible in every stage of learning. In the first phase, with minimum network architecture for realizing competition, information is maximized. In the second phase, a new unit is added, and thereby information is again increased as much as possible. This process continues until no more increase in information is possible. Through greedy information maximization, different Sets of important features in input patterns can be cumulatively discovered in successive stages. We applied our approach to three problems: a dipole problem; a language classification problem; and a phonological feature detection problem. Experimental results confirmed that information maximization can be repeatedly applied and that different features in input patterns are gradually discovered. We also compared our method with conventional competitive learning and multivariate analysis. The experimental results confirmed that our new method can detect salient features in input patterns more clearly than the other methods.
机译：在本文中，我们提出了一种用于竞争学习的新的信息理论方法。这种新方法称为贪婪信息获取，因为网络在学习的每个阶段都尝试吸收尽可能多的信息。在第一阶段，使用最少的网络架构来实现竞争，从而使信息最大化。在第二阶段，添加了一个新单元，从而再次尽可能多地增加了信息。这个过程一直持续到信息不再增加为止。通过贪婪的信息最大化，可以在连续的阶段中累积发现输入模式中重要特征的不同集合。我们将我们的方法应用于以下三个问题：偶极子问题；语言分类问题；和语音特征检测问题。实验结果证实，信息最大化可以重复应用，并且逐渐发现了输入模式中的不同特征。我们还将我们的方法与常规竞争性学习和多元分析进行了比较。实验结果证实，与其他方法相比，我们的新方法可以更清晰地检测输入模式中的显着特征。

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