用成对图像识别训练神经网络，无需复杂公式计算权重。
Neural networks with image recognition by pairs
- 通过成对图像对比来训练网络，替代传统解析计算权重
- 可灵活添加新图像和类别，无需重调原有参数
- 结构简单透明，适合快速扩展识别任务
基于度量识别的神经网络具有严格确定的架构，其神经元数量、连接方式及权重阈值均根据任务初始条件（类别数、样本数、度量表达式）解析计算。本文探讨将此类网络转换为可应用经典学习算法的形式，不再依赖解析公式求解权重。新网络采用成对图像识别方式进行训练，简化了学习过程，并能便捷地通过增加新图像来扩展识别能力。该方法具备四大优势：1）网络架构简单透明；2）训练简便且可靠；3）可使用大量图像进行识别；4）可连续增加可识别类别，而无需修改已有权重与阈值。
原文摘要 · Abstract (English)
Neural networks based on metric recognition methods have a strictly determined architecture. Number of neurons, connections, as well as weights and thresholds values are calculated analytically, based on the initial conditions of tasks: number of recognizable classes, number of samples, metric expressions used. This paper discusses the possibility of transforming these networks in order to apply classical learning algorithms to them without using analytical expressions that calculate weight values. In the received network, training is carried out by recognizing images in pairs. This approach simplifies the learning process and easily allows to expand the neural network by adding new images to the recognition task. The advantages of these networks, including such as: 1) network architecture simplicity and transparency; 2) training simplicity and reliability; 3) the possibility of using a large number of images in the recognition problem using a neural network; 4) a consistent increase in the number of recognizable classes without changing the previous values of weights and thresholds.
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