Testor and Logic Separation in Pattern Recognition
Keywords:
Pattern recognition, Discrete analysis, Testor, Logic separationAbstract
This article is an outline of the first steps of formation of the discrete analytical approach (DAA) of the theoretical pattern recognition (PR) founded by Yu. I. Zhuravlev [2,15]. The “first step” time period covers 1965-75. The DAA domain is further developed into a large number of models and algorithms [1-38]. Hundreds of candidate and tens of doctoral theses were defended in the topic, and thousands of scientific papers were published since then. The essence of DAA is that it is based on the well-developed theory of discrete mathematical analysis and so it is interpretable in terms of input data structures and relations. In parallel to this, several alternative directions such as statistical theory, neural networks, and the structural recognition theory were under development. Today the term machine learning integrates these directions and it appears more frequently. In addition, in pattern recognition area appeared frameworks such as the Deep Learning and Meta-Learning that address, correspondingly, the agile use of HPC and the learning of the learning issues. Deep Learning is based on Deep (multilayer) Neural Networks and so it inherits hardness of knowledge extraction and hardness of interpretability. Meta-Learning is a novel term but in its essence it was addressed in several discrete DAA researches. It is attractive to stay on analysis of the whole PR developments but the aim of our short essay is to compare two core elements of DAA believing that the classic knowledge and theory are enduring and that any further developments will use them in their constructions or in stages of evaluating the result.
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