Learning vector quantization for heterogeneous structured data
نویسندگان
چکیده
In this paper we introduce an approach to integrate heterogeneous structured data into a learning vector quantization. The total distance between two heterogeneous structured samples is defined as a weighted sum of the distances in the single structural components. The weights are adapted in every iteration of learning using gradient descend on the cost function inspired by Generalized Learning Vector Quantization. The new method was tested on a real world data set for pollen recognition using image analysis.
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