Distribution Kernel Methods for Multiple-Instance Learning
By Gary Doran. 2013
4 citations recorded by the source corpus.
I propose to investigate learning in the multiple-instance (MI) framework as a problem of learning from distributions. In many MI applications, bags of instances can be thought of as samples from bag-generating distributions. Recent kernel approaches for learning from distributions have the potential to be successfully applied to these domains and other MI learning problems. Understanding when distribution-based techniques work for MI learning will lead to new theoretical insights, improved algorithms, and more accurate solutions for real-world problems.
Subfields: Statistical Learning Theory
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In Statistical Learning Theory
- Bharath K. Sriperumbudur
- Kenji Fukumizu
- Bernhard Schölkopf
- Krikamol Muandet
- Arthur Gretton
- Dino Sejdinovic
- Le Song
- David Lopez-Paz
Citations in the corpus
- Kernel Mean Embedding of Distributions: A Review and Beyonds (2016, background): “Unlike in the unconditional case, Doran (2013) proposes to learn π(·) from the data in such a way that if π(i) = j, then zi ≈ zj .”