Case-Based Reasoning Tutorial
July 14th, 2012, Berlin/ Germany
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 the tutorial we will explain the case-based reasoning process scheme. We will show what kind of methods are necessary to provide all the functions for such a computer model. We will develop the bridge between CBR and other disciplines. Examples will be given based on signal-interpreting applications and information management.
Program
- 09.00 am: Introduction to CBR
- 10.30 am: Coffee Break
- 10.45 am: Similarity
- 12.15 am: Lunch
- 01.30 pm: Memory Organization
- 03.15 pm: Coffee
- 03.30 pm: CBR in Information Management/Signal Processing
- 05.15 pm: End of Tutorial
Lecturer
Petra Perner is the director of the Institute of Computer Vision and Applied Computer Sciences IBaI. 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. 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.
