A Vector Quantization Approach for Life-Long Learning of Categories
نویسندگان
چکیده
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.
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