An Implicit Memory-Based Method for Supervised Pattern Recognition

Yu Ma,Shafei Wang,Jian Yang,Junan Yang,Yanfei Bao,Zi-Peng Wang

doi:10.1155/2021/4472174

Abstract

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.

Highlights

Pattern recognition methods can be divided into two categories: two-stage and end-to-end
Feature extraction reduces the number of resources that are required to describe a large amount of raw data. e first step is to identify the measurable quantities that make these training sets X1, . . . , Xl distinct from each other. e measurements used for the classification, such as mean value and the standard deviation, are known as features
Some information gets lost since feature extraction is not a lossless compression approach. e lost information cannot be used for pattern recognition. erefore, how to generate features is a fundamental issue

Summary

Introduction

Pattern recognition methods can be divided into two categories: two-stage and end-to-end. Most traditional pattern recognition methods are two-stage: feature extraction and pattern classification [1]. In a supervised pattern recognition task, a set of training data (training set) is used to train a learning procedure. E question arising in the recognition task is why a new data instance can be classified as a particular category. E recognition process depends on the similarity comparison between the current input and the labeled instances. Is paper proposes an implicit memory-based method for supervised pattern recognition. E method does not memorize or recall any labeled instances and is not in the two-stage or end-to-end categories. Compared with the k-nearest neighbors algorithm [19], the proposed method does not need to recall and iterate through the training sets. With the implicit memory model, a recognition algorithm is proposed. 1 s. e expression of a 2-input OR gate can be expressed as follows: Y Z1 + Z2. e output Y of an OR gate is 1 when any one or more of the inputs are 1 s

Model of Implicit Memory

Experiment

Conclusion

Findings

Proof of Lemma 1