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A method for reducing the amounts of training samples for developing AI systems

机译:一种减少用于开发AI系统的训练样本数量的方法

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摘要

A lot of tools are developed for AI (Artificial Intelligent) development. These tools are easy to use and the number of kinds of the tools are increasing quickly with new research results, therefore they are widely utilized for AI development in nowadays. A research issue here we need to solve is to provide methods for reducing training samples for AI development. The research issue comes from the background that most of the AI systems developed by using AI developing tools require a huge amount of training samples. In this paper, we propose a method for reducing the amount of training samples. Based on the proposed method, we created a Japanese hand-writing recognizing system to evaluate the effectiveness of the proposed method. This system is used for recognizing more than 6,000 different kinds of Japanese Kanji characters. The important feature of the system is that we do not need to collect millions of hand-writing Kanji character images as training samples. The effectiveness of the proposed method is confirmed by demonstration experiments.
机译:已开发了许多用于AI(人工智能)开发的工具。这些工具易于使用,并且随着新的研究成果,工具的种类正在迅速增加,因此在当今的AI开发中已被广泛使用。我们需要解决的一个研究问题是提供减少AI开发训练样本的方法。研究问题来自这样一个背景,即使用AI开发工具开发的大多数AI系统都需要大量的训练样本。在本文中,我们提出了一种减少训练样本量的方法。基于所提出的方法,我们创建了日语手写识别系统来评估所提出方法的有效性。该系统用于识别6,000多种日文汉字字符。该系统的重要特征是我们不需要收集数以百万计的手写汉字字符图像作为训练样本。演示实验证实了该方法的有效性。

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