Case-based object recognition requires a general case of the object that should be detected. Real world applications such as the recognition of biological objects in images cannot be solved by one general case. A case-base is necessary to handle the great natural variation in appearance of these objects. We present our conceptual clustering algorithm to learn a hierarchy of decreasingly generalized cases from a set of acquired structural cases. Due to its concept description, it explicitly supplies for each cluster a generalized case and a measure for the degree of its generalization. The resulting hierarchical case base is used for applications in the field of case-based object recognition. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Jänichen, S., & Perner, P. (2005). Learning of general cases. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3776 LNCS, pp. 774–779). Springer Verlag. https://doi.org/10.1007/11590316_125
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