Abstract
We present a category learning vector quantization (cLVQ) approach for incremental and life-long learning of multiple visual categories where we focus on approaching the stability-plasticity dilemma. To achieve the life-long learning ability an incremental learning vector quantization approach is combined with a category-specific feature selection method in a novel way to allow several metrical "views" on the representation space for the same cLVQ nodes. © 2009 Springer Berlin Heidelberg.
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CITATION STYLE
Kirstein, S., Wersing, H., Gross, H. M., & Körner, E. (2009). A vector quantization approach for life-long learning of categories. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5506 LNCS, pp. 805–812). https://doi.org/10.1007/978-3-642-02490-0_98
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