The Science of Pattern Recognition:Achievements and Perspectives.doc

The Science of Pattern Recognition:Achievements and Perspectives.doc

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The Science of Pattern Recognition:Achievements and Perspectives

The Science of Pattern Recognition Achievements and Perspectives Robert P.W. Duin1 and El˙zbieta Pekalska2 1 ICT group, Faculty of Electr. Eng., Mathematics and Computer Science Delft University of Technology, The Netherlands r.duin@ieee.org 2 School of Computer Science, University of Manchester, United Kingdom pekalska@cs.man.ac.uk Summary. Automatic pattern recognition is usually considered as an engineering area which focusses on the development and evaluation of systems that imitate or assist humans in their ability of recognizing patterns. It may, however, also be considered as a science that studies the faculty of human beings (and possibly other biological systems) to discover, distinguish, characterize patterns in their environment and accordingly identify new observations. The engineering approach to pattern recognition is in this view an attempt to build systems that simulate this phenomenon. By doing that, scientific understanding is gained of what is needed in order to recognize patterns, in general. 自动模式识别通常被认为是这样的一个工程领域:专注于开发和评价模仿或辅助人类识别模式能力的系统,但是也可能被认为是这样的一门科学:学习人类(或其它生物系统)在所处环境中发现、区别和找出特征从而标识出观察结果的本领。模式识别中工程的观点是试图建立模拟生物识别能力的系统,通过工程中的实践,总的来说,科学上的理解在模式识别中的技术需求方面得到了发展。 Like in any science understanding can be built from different, sometimes even opposite viewpoints. We will therefore introduce the main approaches to the science of pattern recognition as two dichotomies of complementary scenarios. They give rise to four different schools, roughly defined under the terms of expert systems, neural networks, structural pattern recognition and statistical pattern recognition. 象任何科学一样,对模式识别的理解能够从不同方向来建立,有时甚至是相反的观点。我们将介绍模式识别科学中的主要方法,即两种不同方向且各有两个不同种类的技术,这些技术产生了四个不同学派,粗略地可以定义为:专家系统,神经网络,结构模式识别和统计模式识别。 We will briefly describe what has been achieved by these schools, what is common and what is specific, which limitations are encountered and which perspectives arise for the future. Finally, we will focus on the challenges facing pattern recognition in the dece

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