Evolvable pattern recognition systems

背景资料。Project CellNet(Kharma等人，2004年)是为数不多的几个研究项目之一，旨在生产通用模式识别系统，该系统可以使用遗传编程(GP)和一些训练措施来配置用于特定的识别任务。当前版本的CellNet(CellNet-CoEv)仍然是自动合成完整多分类器的中间步骤。这些特征仍然是硬编码的：我们打算用一个基于GP的无参数特征创建模块来取代它们。此外，下一版本的CellNet将生成完整的多分类器。方法。我们打算在总体上保持对利用遗传编程进行特征创建和选择以及分类器合成的重视。然而，我们还将在下一版本的蜂窝网络中引入其他技术。将进行一些认真的预处理，以创建每类图案的原型和图像。这之后将通过GP自动创建，通过统计技术进行评估，并使用特征检测器。最后，选定的特征将由分类器使用，分类器将使用标准模式数据库进行彻底评估。贡献。预期的主要贡献是：(A)产生一个完全可进化的多类模式识别系统，该系统可处理一系列真实世界的符号/信号分类任务；(B)在模式识别领域开发适合于自动特征创建问题的一些前沿遗传编程技术；(C)在适当的机制和初始条件下，更好地理解较简单的形式如何产生复杂性。参考文献。首页--期刊主要分类--期刊细介绍--期刊题录与文摘--期刊详细文摘内容《细胞网络计划：发展自主模式识别器》，《国际模式识别与人工智能杂志》，第18卷，第6期(2004)1-18。

BACKGROUND. Project CellNet (Kharma et al., 2004) is one of a handful of research projects that aim at producing generic Pattern Recognition systems, which can be configured for particular recognition tasks, using Genetic Programming (GP) and some measure of training.   MOTIVATION. The current version of CellNet (CellNet-CoEv) is still an intermediate step on the path of automatic synthesis of a complete multi-classifier. The features are still hard-coded: we intend to replace them with a parameterless GP-based feature creation module. Also, the next version of CellNet will produce complete multi classifiers. METHOD. We intend to maintain the overall emphasis on the use of Genetic Programming for the purpose of feature creation and selection, as well as classifier synthesis. However, we will  introduce other techniques to the next version of CellNet. Some serious pre-processing will be carried out to create a prototypical sum image per class of patterns. This will be followed by the automatic creation via GP, evaluation via statistical techniques, and employment of feature detectors. Finally, the selected features will be used by a classifier, which will be thoroughly assessed using standard pattern databases. CONTRIBUTIONS. The main anticipated contributions are: (a) The production of a fully evolvable multi-class pattern recognition system, which can deal with a range of real-world symbol/signal classification tasks; (b) The development of some leading-edge Genetic Programming techniques, suited to the problem of automatic Feature Creation, in the area of Pattern Recognition; (c) A better understanding of how complexity can arise from simpler forms, given the right mechanisms and initial conditions. REFERENCES. Kharma, N., Kowaliw, T., Clement, E., Jensen, C., Youssef, A., and Yao, J. "Project CellNet: Evolving an Autonomous Pattern Recognizer", International Journal of Pattern Recognition and Artificial Intelligence, Vol. 18, No. 6 (2004) 1-18.