|
HKUST Institutional Repository >
Computer Science and Engineering >
CSE Master Theses >
Please use this identifier to cite or link to this item:
http://hdl.handle.net/1783.1/5830
|
| Title: | Kernel-based multiple-instance learning |
| Authors: | Cheung, Pak-Ming |
| Issue Date: | 2006 |
| Abstract: | In recent years, the Multiple-Instance Learning (MIL) problem is becoming more and more popular in the machine learning community. Each training object (bag) of the MIL problem is a set of patterns (instances). Label information is only associated with the bags, but not with their constituent instances. Moreover, a positive bag must have at least one positive instance, but may have many neg-ative instances. Since we can only access the label information of a bag and a positive bag may have many negative instances, MIL is more challenging than the traditional supervised learning (or single-instance learning). On the other hand, it is fruitful to study MIL, since many real-world problems such as drug activity prediction are inherently MI problems which cannot be generalized well under the traditional single-instance learning model. In addition, the generaliza-tion performance of many single-instance learning problems, e.g., Content-based Image Retrieval (CBIR), are found to be improved when they are casted into an appropriate MIL representation.
In this thesis, I study MIL algorithms based on kernel methods. In particular, I focus on support vector machines, which have been highly successful in many machine learning problems. This thesis first discusses how to re-formulate the SVM to adapt to the MI problem setting by utilizing both the bag and instance information at the same time. After that, I propose how to define a MI kernel over bags based on the marginalizing kernel. The resulted bag kernel can then be used in a standard SVM. I also extend this marginalized kernel to the real-valued regression setting, which is more and more popular in the MIL community. Empirical results show that the proposed methods have better performance over various traditional methods. |
| Description: | Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2006
x, 47 leaves : ill. ; 30 cm
HKUST Call Number: Thesis COMP 2006 CheungP |
| URI: | http://hdl.handle.net/1783.1/5830 |
| Appears in Collections: | CSE Master Theses
|
Files in This Item:
| File |
Description |
Size | Format |
|---|
| th_redirect.html | | 0Kb | HTML | View/Open |
|
All items in this Repository are protected by copyright, with all rights reserved.
|
