How the human brain does recognition is still an open question. No physical or biological experiment can fully reveal this process. Psychological evidence is more about describing phenomena and laws than explaining the physiological processes behind them. The need for interpretability is well recognized. This paper proposes a new method for supervised pattern recognition based on the working pattern of implicit memory. The artificial neural network (ANN) is trained to simulate implicit memory. When an input vector is not in the training set, the ANN can treat the input as a “do not care” term. The ANN may output any value when the input is a “do not care” term since the training process needs to use as few neurons as possible. The trained ANN can be expressed as a function to design a pattern recognition algorithm. Using the Mixed National Institute of Standards and Technology database, the experiments show the efficiency of the pattern recognition method.

中文翻译：

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一种基于隐式记忆的有监督模式识别方法

人类大脑如何进行识别仍然是一个悬而未决的问题。没有任何物理或生物实验可以完全揭示这个过程。心理证据更多的是描述现象和规律，而不是解释它们背后的生理过程。对可解释性的需求是公认的。本文提出了一种基于内隐记忆工作模式的有监督模式识别新方法。人工神经网络 (ANN) 被训练来模拟内隐记忆。当输入向量不在训练集中时，ANN 可以将输入视为“无关”项。当输入是“不关心”时，ANN 可以输出任何值”术语，因为训练过程需要使用尽可能少的神经元。训练好的 ANN 可以表示为一个函数来设计模式识别算法。使用混合国家标准与技术研究所数据库，实验显示了模式识别方法的效率。