Keynote Lecture

Case-Based Reasoning and the Statistical Challenges

Prof. Petra Perner

Prof. Dr Petra Perner

Institute of Computer Vision and Applied Computer Sciences, Germany
www.ibai-institut.de www.ibai-research.de

Abstract

Case-based Reasoning (CBR) solves problems using the already stored knowledge, and captures new knowledge, making it immediately available for solving the next problem. Therefore, case-based reasoning can be seen as a method for problem solving, and also as a method to capture new experience and make it immediately available for problem solving. It can be seen as a learning and knowledge-discovery approach, since it can capture from new experience some general knowledge, such as case classes, prototypes and some higher-level concept.

The idea of case-based reasoning originally came from the cognitive science community which discovered that people are rather reasoning on formerly successfully solved cases than on general rules. The case-based reasoning community aims to develop computer models that follow this cognitive process. For many application areas computer models have been successfully developed, which were based on CBR, such as signal/image processing and interpretation tasks, help-desk applications, medical applications and E-commerce product-selling systems.

In this talk we will explain the case-based reasoning process scheme. We will show what kinds of methods are necessary to provide all the functions for such a computer model. We will develop the bridge between CBR and Statistics. Examples will be given based on signal-interpreting applications. Finally, we will show recent new developments and we will give an outline for further work.

Brief biography of the Speaker

Petra Perner is the director of the Institute of Computer Vision and Applied Computer Sciences IBaI in Leipzig, Germany. She received her Diploma degree in electrical engineering in 1981, her PhD degree in computer science in 1985, and in 1989 she finished her habilitation thesis entitled "A Methodology for the Development of Knowledge-Based Image-Interpretation Systems".

She has been the principal investigator of various national and international research projects. She received several research awards for her research work and has been awarded with 3 business awards for her work on bringing intelligent image interpretation methods and data mining methods into business. Her research interest is image analysis and interpretation, machine learning, data mining, machine learning, image mining and case-based reasoning. Recently, she is working on various medical, chemical and biomedical applications, information management applications, technical diagnosis and e-commerce applications. Most of the developments are protected by legal patent rights and can be licensed to qualified industrial companies. She has published numerous scientific publications and patents and is often requested as a plenary speaker in distinct research fields as well as across disciplines. Her vision is to build intelligent flexible and robust data-interpreting systems that are inspired by the human case-based reasoning process.

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