書誌事項
- タイトル別名
-
- Self-measuring Similarity for Multi-task Gaussian Process
抄録
Multi-task learning aims at transferring knowledge between similar tasks. The multi-task Gaussian process framework of Bonilla et al. models (incomplete) responses of C data points for R tasks (e.g., the responses are given by R × C matrix) by a Gaussian process; the covariance function is defined as the product of a covariance function on input-dependent features and the inter-task covariance matrix (which is empirically estimated as a model parameter). We extend this framework by incorporating a novel similarity measurement, which allows for the representation of much more complex data structures. The proposed framework also enables us to exploit additional information (e.g., the input-dependent features) by constructing the covariance matrices with combining them on the covariance function. We also derive an efficient learning algorithm to make prediction by using an iterative method. Finally, we apply our model to a real data set of recommender systems and show that the proposed method achieves the best prediction accuracy on the data set.
収録刊行物
-
- 人工知能学会論文誌
-
人工知能学会論文誌 27 (3), 103-110, 2012
一般社団法人 人工知能学会
- Tweet
詳細情報 詳細情報について
-
- CRID
- 1390282680083477760
-
- NII論文ID
- 130001878751
-
- BIBCODE
- 2012TJSAI..27..103H
-
- ISSN
- 13468030
- 13460714
-
- 本文言語コード
- ja
-
- データソース種別
-
- JaLC
- Crossref
- CiNii Articles
- KAKEN
-
- 抄録ライセンスフラグ
- 使用不可