講演抄録/キーワード |
講演名 |
2017-06-25 09:30
t指数型分布族に対する期待値伝播法 ○二見 太・佐藤一誠(東大/理研)・杉山 将(理研/東大) IBISML2017-6 |
抄録 |
(和) |
(まだ登録されていません) |
(英) |
Exponential family distributions are highly useful in machine learning since their calculation can be performed efficiently through natural parameters. The exponential family has recently been extended to the t-exponential family, which contains Student-t distributions as family members and thus allows us to handle noisy data well. However, since the t-exponential family is defined by the deformed exponential, we cannot derive an efficient learning algorithm for the t-exponential family such as expectation propagation (EP). In this paper, we borrow the mathematical tools of q-algebra from statistical physics and show that the pseudo additivity of distributions allows us to perform calculation of t-exponential family distributions through natural parameters. We then develop an expectation propagation (EP) algorithm for the t-exponential family, which provides a deterministic approximation to the posterior or predictive distribution with simple moment matching. We finally apply the proposed EP algorithm to the Bayes point machine and Student-t process classification, and demonstrate their performance numerically. |
キーワード |
(和) |
/ / / / / / / |
(英) |
exponential family / Student-t / Gaussian / pseudo additivity / expectation propagation / assumed density filter / gaussian process / |
文献情報 |
信学技報, vol. 117, no. 110, IBISML2017-6, pp. 179-184, 2017年6月. |
資料番号 |
IBISML2017-6 |
発行日 |
2017-06-17 (IBISML) |
ISSN |
Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 |
著作権に ついて |
技術研究報告に掲載された論文の著作権は電子情報通信学会に帰属します.(許諾番号:10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) |
PDFダウンロード |
IBISML2017-6 |
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