講演抄録/キーワード |
講演名 |
2016-11-17 14:00
正例とラベルなしデータからの分類に基づく半教師付き分類 ○坂井智哉・ドゥ・プレシ マーティヌス・クリストフェル・ニウ ガン(東大)・杉山 将(理研/東大) IBISML2016-80 |
抄録 |
(和) |
(まだ登録されていません) |
(英) |
Most of the semi-supervised learning methods developed so far use unlabeled data for regularization purposes under particular distributional assumptions such as the manifold assumption. On the other hand, recently developed methods of emph{learning from positive and unlabeled data} (PU learning) use unlabeled data for loss evaluation, i.e., label information is directly extracted from unlabeled data. In this paper, we extend PU learning to also incorporate negative data and propose a novel semi-supervised learning approach. We establish a generalization error bound for our novel method and show that the bound decreases with respect to the number of unlabeled data emph{without} the distributional assumptions that are required in existing semi-supervised learning methods. Through experiments, we demonstrate the usefulness of the proposed method. |
キーワード |
(和) |
/ / / / / / / |
(英) |
Semi-Supervised Learning / Learning from Positive and Unlabeled Data / / / / / / |
文献情報 |
信学技報, vol. 116, no. 300, IBISML2016-80, pp. 243-250, 2016年11月. |
資料番号 |
IBISML2016-80 |
発行日 |
2016-11-09 (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 |
IBISML2016-80 |