| 摘要 |
PROBLEM TO BE SOLVED: To much more simply learn a learning parameter for predicting probability, a route, and an elapsed time leading to a specific place or a place where a specific action is performed in the future following the current time. SOLUTION: A state series generation part 72 generates state series data by converting the time series data of position data into the time series data of a state node<SB>si</SB>by a user activity model based on a parameter supplied from a model learning part 71. A parameter calculation part 62 calculates the parameter of the stochastic state transition model of the movement route and action mode of a user by calculating the parameter of time-series data corresponding to the appearance frequency of the state node, the appearance frequency of inter-state node transition and the state nodes by using the state series data and the time series data of the action mode. For example, this invention may be applied to a learning unit which learns the parameter of a learning model for predicting the destination and movement route of the user. COPYRIGHT: (C)2011,JPO&INPIT
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