Identifying Multi-instance Outliers

By Ou Wu, Jun Gao, Weiming Hu, Bing Li, Mingliang Zhu. 2010

17 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Han-Jia Ye
2. Xiu-Shen Wei
3. Da-Wei Zhou
4. Chunhua Shen
5. Dan Zhang
6. Jianxin Wu
7. Zhi-Hua Zhou
8. Luo Si

Citations in the corpus

• Learning Theory for Distribution Regression (2014, methodology): “…Zhang et al., 2009, 2011; Chen and Wu, 2012), (iii) ranking (Bergeron et al., 2008; Hu et al., 2008; Bergeron et al., 2012), (iv) outlier detection (Wu et al., 2010), (v) transfer learning (Raykar et al., 2008; Zhang and Si, 2009), or (vi) feature selection, -weighting and -extraction (also…”
• Fast Bundle Algorithm for Multiple-Instance Learning (2012, unknown): “Ç”
• Multi-Instance Learning With Emerging Novel Class (2021, background): “I-MLOF: Instance-neighborhood based multi-instance outlier detector [61] is a representative outlier detection algorithm in the multi-instance setting.”
• MILEAGE: Multiple Instance LEArning with Global Embedding (2013, methodology): “Numerous methods have been developed for MIL classification (Andrews et al., 2003; Dietterich et al., 1998; Kim & la Torre, 2010) and its variants, such as outlier detection (Wu et al., 2010), online learning (Babenko et al., 2011), ranking (Hu et al., 2008), etc.”