Metric Adaptation for Optimal Feature Classification in Learning Vector Quantization Applied to Environment Detection
نویسنده
چکیده
The paper deals with the concept of relevance learning in learning vector quantization. Recent approaches are considered: the generalized learning vector quantization as well as the soft learning vector quantization. It is shown that relevance learning can be included in both methods obtaining similar structured learning rules for prototype learning as well as relevance factor adaptation. We show the power in case of image classification of natural environment images. The performance makes the tool suitable for classification tasks in robotics.
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