Evolvable pattern recognition systems

背景。CellNet项目（Kharma et al., 2004）是少数几个旨在生产通用模式识别系统的研究项目之一，该系统可以使用遗传编程（GP）和一些训练措施来配置特定的识别任务。动机。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。这些特性仍然是硬编码的：我们打算用一个无参数的基于gp的特性创建模块来取代它们。此外，CellNet的下一个版本将产生完整的多分类器。方法。我们打算总体上强调使用遗传规划来进行特征创建和选择，以及分类器合成。然而，我们将介绍其他技术到下一个版本的CellNet。一些严肃的预处理将进行，以创建一个原型和图像的每一类模式。接下来是通过GP自动创建，通过统计技术进行评估，以及使用特征检测器。最后，选择的特征将由分类器使用，该分类器将使用标准模式数据库进行彻底评估。的贡献。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，它可以处理一系列现实世界的符号/信号分类任务；(b)发展适合于模式识别领域自动创造特征问题的一些前沿遗传程序设计技术；(c)在适当的机制和初始条件下，更好地了解如何从较简单的形式产生复杂性。参考文献。卡尔玛，N.，科瓦里，T.，克莱门特，E.，詹森，C.，优素福，A.，和姚，J.。“CellNet项目：发展自主模式识别器”，《国际模式识别与人工智能杂志》，Vol. 18, No. 6(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.