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このアイテムの引用には次の識別子を使用してください: http://hdl.handle.net/10445/6869

タイトル: 自己計測類似度を用いたマルチタスクガウス過程
著者: 林, 浩平
竹之内, 高志
冨岡, 亮太
鹿島, 久嗣
アブストラクト: 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.
研究業績種別: 原著論文/Original Paper
資料種別: Journal Article
査読有無: あり/yes
単著共著: 共著/joint
発表雑誌名,発表学会名など: 人工知能学会論文誌
巻: 27
号: 3
開始ページ: 103
終了ページ: 110
年月日: 2012年1月1日
出版社: 人工知能学会
出現コレクション:竹之内 高志





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