![]() ![]() In this paper, we propose a Gaussian process- hidden semi-Markov model (GP-HSMM) that can divide continuous time series data into segments in an unsupervised manner. This capacity for unsupervised segmentation is also useful for robots, because it enables them to flexibly learn languages, gestures, and actions. People can divide continuous information into segments without using explicit segment points. Analogously, continuous motions are segmented into recognizable unit actions. For example, humans can segment speech waves into recognizable morphemes. Humans divide perceived continuous information into segments to facilitate recognition. Nakamura, Tomoaki Nagai, Takayuki Mochihashi, Daichi Kobayashi, Ichiro Asoh, Hideki Kaneko, Masahide Segmenting Continuous Motions with Hidden Semi-markov Models and Gaussian Processes
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