Multiple instance ranking

By Charles Bergeron, Jed Zaretzki, Curt M. Breneman, Kristin P. Bennett. 2008

46 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Signal Processing

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In Computer Networks and Communications

In Computer Vision and Pattern Recognition

In Signal Processing

Citations in the corpus

Learning Theory for Distribution Regression (2014, background): “…2002; Zhou et al., 2009; Kwok and Cheung, 2007), (ii) clustering (Zhang and Zhou, 2009; 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.,…”
APRIL: Active Preference-learning based Reinforcement Learning (2012, neutral): “Along this line, a multipleinstance ranking setting [3] could be used to learn preferences at the fragment”
Person Re-identification by Video Ranking (2014, background): “(6), we relax v to be continuous with non-negative entry and unity 1 norm as in [4].”
Identifying Multi-instance Outliers (2010, unknown): “Many traditional learning tasks have been brought into multi-instance framework and yields several new machine learning issues such as multi-instance ranking [14, 17], multi-instance clustering [18, 19], etc.”
Learning joint query interpretation and response ranking (2012, background): “…entity ranking accuracy of a reasonable query interpreter will be between the “lower bound” of a generic system that makes no effort to identify the target type (i.e., all catalog entities are candidates), and the upper bound of an unrealistic “perfect” system that knows the target type by magic.”
Real-World Anomaly Detection in Surveillance Videos (2018, methodology): “[8] proposed an algorithm for solving multiple instance ranking problems using successive linear programming and demonstrated its application in hydrogen abstraction problem in computational chemistry.”
Knowledge Graph and Corpus Driven Segmentation and Answer Inference for Telegraphic Entity-seeking Queries (2014, methodology): “Similar distant supervision problems were posed via bundle method by (Bergeron et al., 2008), and (Yu and Joachims, 2009), who used CCCP (Yuille and Rangarajan, 2006).”
Multiple instance learning: A survey of problem characteristics and applications (2016, methodology): “Thus, many methods using SVM and SVM-like algorithms [4, 6, 27, 179, 181, 182] rapidly become impractical as the number of instances increases [46].”
Programming by Feedback (2014, background): “The identification of the behavioral subsequences responsible for the user’s feedback can be formalized in terms of multiple instance ranking (Bergeron et al., 2008), considering a long behavior as a set of subbehaviors where a few sub-behaviors are responsible for the user’s preferences.”
Fast Bundle Algorithm for Multiple-Instance Learning (2012, unknown): “Ç”