Evolvable pattern recognition systems

背景CellNet项目（Kharma等人，2004年）是少数几个旨在产生通用模式识别系统的研究项目之一，这些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。这些功能仍然是硬编码的：我们打算用一个无参数的基于GP的功能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。法我们打算保持对遗传编程的使用的整体重点的特征创建和选择，以及分类器合成的目的。但是，我们将在下一个版本的CellNet中引入其他技术。一些严重的预处理将进行创建一个原型和图像的每类模式。随后将通过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，所选的功能将用于分类，这将是彻底评估使用标准模式数据库。捐款.主要的预期贡献是：（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）在模式识别领域发展一些适合于自动特征创建问题的前沿遗传编程技术;（c）更好地理解在适当的机制和初始条件下，简单的形式如何产生复杂性。参考资料。Kharma，N.，Kowaliw，T.，Clement，E.，詹森，C.，Youssef，A.，和Yao，J.“Project CellNet：Evolving an Autonomous Pattern Recognizer”，International Journal of Pattern Recognition and Artificial Intelligence，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.