| 摘要 |
PROBLEM TO BE SOLVED: To promptly generate algorithm allowing extraction of a corresponding characteristic amount from content data such as musical piece data with high accuracy. SOLUTION: In this information processor, a high-level characteristic amount extraction type learning part uses a low-level characteristic amount included in a Pth initial group and data for learning among teacher data, applies them to ath learning algorithm, and estimates a high-level characteristic amount extraction type by learning in a step S94. In a step S95, the high-level characteristic amount extraction type learning part calculates an information amount criterion AIC (Akaike Information Criterion) or BIC (Bayesian Information Criterion) as an evaluation value of a high-level characteristic amount obtained as a processing result of the step S94. In a step S97, the high-level characteristic amount extraction type learning part genetically updates the initial group of a p set comprising the low-level characteristic amount used for the learning on the basis of the evaluation value (the information amount criterion). The information processor can be applied to a system acquiring a high-level characteristic amount of a musical piece or a video. COPYRIGHT: (C)2007,JPO&INPIT
|
