确定二元对象亲和函数的神经网络

2020 IEEE KhPI Week on Advanced Technology (KhPIWeek) Pub Date : 2020-10-05 DOI:10.1109/KhPIWeek51551.2020.9250162

Dmitrienko Valery Dmitrievich, Leonov Sergey Yurievich, Zakovorotniy Alexander Yurievich, Mezentsev Nikolay Viktorovich

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引用次数: 0

摘要

提出了一种综合神经网络的方法，通过对物体属性进行双极编码，对物体进行比较、识别和分类。由于使用接近函数对离散对象应用比Hemming距离更精细的接近属性，该方法拓宽了神经元网络解决识别和分类任务的适用范围。

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Neural networks for determining affinity functions of binary objects

A new method of synthesizing neural network for comparing, identifying, and classifying various objects through bipolar encoding of their attributes is offered. The mentioned method broadens the neuron network applicability sphere for solving tasks of identification and classification due to the use of proximity functions applying finer proximity attributes for discrete objects than the Hemming's distance.

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来源期刊

2020 IEEE KhPI Week on Advanced Technology (KhPIWeek)

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