Multiple Instance Learning: Algorithms and Applications

By Boris Babenko. 2008

How researchers see this paper

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

1. Bharath K. Sriperumbudur
2. Krikamol Muandet
3. Bernhard Schölkopf
4. Arthur Gretton
5. Barnab'as P'oczos
6. Kenji Fukumizu
7. Zoltán Szabó
8. David Lopez-Paz

Citations in the corpus

• Learning Theory for Distribution Regression (2014, methodology): “The MIL approach has been applied in several domains; see the reviews from Babenko (2004); Zhou (2004); Foulds and Frank (2010); Amores (2013).”
• Multiple instance learning: A survey of problem characteristics and applications (2016, unknown): “In 2008, Babenko published a report [14] containing an updated survey of the main families of MIL methods, and distinguished two types of ambiguity in MIL problems.”
• Thoracic Disease Identification and Localization with Limited Supervision (2017, methodology): “In multiple instance learning (MIL), an input is a labeled bag (e.g., an image) with many instances (e.g., image patches) [3].”