| 摘要 |
PROBLEM TO BE SOLVED: To much more accurately learn a hidden Markov Model from missing time-series data. SOLUTION: A data interpolation part 31 interpolates data missing in time-series data. A state likelihood calculation part 32 and a backward likelihood calculation part 35 calculate state likelihood with respect to normal position data and state likelihood with respect to interpolation position data on different conditions, and calculates the likelihood of a hidden Markov Model with respect to the time-series data obtained by interpolating the data. An initial probability transition probability estimation part 36 to an observation probability estimation part 38 estimate the hidden Markov Model from the time-series data, and update the hidden Markov Model so that the likelihood to be calculated by the state likelihood calculation part 32 to the backward likelihood calculation part 35 can be increased. For example, this invention may be applied to a learning device which learns the activity model of a user. COPYRIGHT: (C)2011,JPO&INPIT
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