基于GP的模式识别分类器的发展问题

A. Teredesai, V. Govindaraju
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引用次数: 25

摘要

本文讨论了进化遗传规划(GP)分类器用于手写体数字识别等模式识别任务时的问题。开发优雅的手写数字分类解决方案是一项具有挑战性的任务。同样,与其他传统技术相比,使用遗传规划设计和训练分类器是模式识别中相对较新的方法。概述了全科医生培训的几种策略,并报告了实证观察结果。我们所面临的问题,如训练时间,各种健身景观和结果的准确性进行了讨论。注意已采取测试GP使用各种参数和几个手写数字数据集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Issues in evolving GP based classifiers for a pattern recognition task
This paper discusses issues when evolving genetic programming (GP) classifiers for a pattern recognition task such as handwritten digit recognition. Developing elegant solutions for handwritten digit classification is a challenging task. Similarly, design and training of classifiers using genetic programming is a relatively new approach in pattern recognition as compared to other traditional techniques. Several strategies for GP training are outlined and the empirical observations are reported. The issues we faced such as training time, a variety of fitness landscapes and accuracy of results are discussed. Care has been taken to test GP using a variety of parameters and on several handwritten digits datasets.
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