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- W2120227597 abstract "New input devices need clever algorithms to process input information. We constructed a hierarchically structured neural network assembly based on recurrent self-organizing maps which is able to process and to classify motion data. We derived motion data using a so called Gesture Cube (M. Kranz et al., 2006), a cubic tangible user interface developed for one-handed control of media appliances in a home environment. This previously recorded data was automatically pre-processed by our biologically inspired neural network and classified by a improved k-nearest neighborhood classifier. In this paper we shortly describe the platform used for data acquisition but focus on the novel algorithms used for classification." @default.
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- W2120227597 date "2007-06-01" @default.
- W2120227597 modified "2023-09-27" @default.
- W2120227597 title "Gesture Classification with Hierarchically Structured Recurrent Self-Organizing Maps" @default.
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- W2120227597 doi "https://doi.org/10.1109/inss.2007.4297394" @default.
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