God... I see the name again... James T. Kwok 郭天佑. Although he's an associate professor, I have heard him very active in the field of machine learning. And last November I saw him in the conference held in Nanjing for native experts in this area. The name of the first author... I thought he was the one Tak-ming Chan :-(
In this paper, I find more information on multiple instance learning, esp. formal formulation of the problem. Thanks to Prof. Zhou's talk for us, I follow the idea of the authors without much trouble. However, as is introduced in this paper, many SVM-based algorithms in MIL were proposed in the past few years, which I am not quite familiar with. So I listed several literatures at the end from the refereces for future study.
The authors emphasize that they put not only bag regularizers but also instance regularizers to the original SVM objective function. Then they prove the representer theorem in this case so as to formulate the dual form. In the latter one, it can be easily seen that the optimization is not convex. There is a DC-style constraint. So C3P can be applied. Later they examine the regression problem as well.
I decide to stop scanning for a while(in a week's time). I have to peruse the SVM theory and these related stuffs. I guess then I will review all these papers and see whether I can understand better.
Here are several references:
- Support vector machines for multiple-instance learning, by S. Andrew and et al., in NIPS 2003
- Solving the multiple instance problem with axis-parallel rectangles, by T. Dietterich and et al, in AI
- Supervised versus multiple instance learning: An empirical comparison, by S. Ray and et al., in ICML 2005
- Multiple Instance Regression, by S. Ray and et al., ICML 2001
- EM-DD: An improved multiple-instance learning, by Zhang Q., in NIPS
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