06月12日(Tue) 15:30〜20:00 K会場(-ゆ～あいプラザ山口県社会福祉会館／第1会議室(81))
| 演題番号 | 1K2-IOS-1b-5 |
|---|---|
| 題目 | A Density-based Approach for Positive and Unlabeled Learning |
| 著者 | Nattee Cholwich(Sirindhorn International Institute of Technology, Thammasat University) Khamsemanan Nirattaya(Sirindhorn International Institute of Technology, Thammasat University) Theeramunkong Thanaruk(Sirindhorn International Institute of Technology, Thammasat University) Numao Masayuki(Osaka University) |
| 時間 | 06月12日(Tue) 17:40〜18:10 |
| 概要 | Positive and Unlabeled learning (PU learning) is a machine learning approach that focuses on generating a two-class classification model using only a set of positive examples, and a set of unlabeled examples. Various techniques have been proposed for PU learning. Most of the techniques try to detect a group of reliables negative examples from the given unlabeled examples. Then, a classification model can be incrementally built. In this paper, we propose a new technique for detecting the reliable negative examples based on the density of examples in the search space. |
| 論文 | PDFファイル |