Titlebook: Case-Based Reasoning; A Textbook Michael M. Richter,Rosina O. Weber Textbook 2013 Springer-Verlag GmbH Germany 2013 Artificial intelligence

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Uncertaintyly in applications. Of special interest is the relation to similarity. The rough set concept provides a method of describing uncertain results in a user-controlled way. Fuzzy sets and their logical descriptions are introduced. Examples show the relations between fuzzy sets and CBR. In addition, poss

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Probabilitiesenomena. Some basic knowledge about probabilities is required. We discuss that the connections between similarities and probabilities are manifold. There are two directions: Probabilities give rise to adequate similarity measures. First we introduced covariance and correlation measures and the Kullb

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https://doi.org/10.1007/978-3-642-40167-1Artificial intelligence; Case-based reasoning (CBR); Complex knowledge; Information retrieval; Knowledge

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abstemious

Michael M. Richter,Rosina O. WeberFirst English-language textbook on the topic.Coauthor among the pioneers of the subject.Content thoroughly class-tested, book features chapter summaries, background notes, and exercises throughout.Inc

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Advances in Information Technologies,ly an interest in learning about it. It e positions case-based reasoning (CBR) in its scientific, educational, and applied contexts; it is therefore crucial for anyone interested in learning or teaching CBR. It provides a very intuitive notion of the CBR reasoning paradigm that can be understood by

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