Mining ambiguous data with multi-instance multi-label representation
Abstract
In traditional data mining and machine learning settings, an object is represented by an instance (or feature vector) which is associated with a class label. However, real-world data are usually ambiguous and an object may be associated with a number of instances and a number of class labels simultaneously. For example, an image usually contains multiple salient regions each can be represented by an instance, while in image classification such an image can belong to several classes such as lion, grassland and tree simultaneously. Another example is text categorization, where a document usually contains multiple sections each can be represented as an instance, and the document can be regarded as belonging to different categories such as scientific novel, Jules Verne's writing or even books on travelling simultaneously. Web mining is another example, where each of the links or linked pages can be regarded as an instance while the web page itself can be recognized as a news page, sports page, soccer page, etc. This talk will introduce a new learning framework, multi-instance multi-label learning (MIML), which is a choice in addressing such kind of problems.
Mining ambiguous data with multi-instance multi-label representation
Zhi-Hua Zhou
LAMDA Group,
National Key Laboratory for Novel Software Technology,
Nanjing University, China
http://cs.nju.edu.cn/zhouzh/
Mining Ambiguous Data with Multi-Instance
Multi-Label Representation
http://lamda.nju.edu.cn
A Question
What is the most difficult task for current
computer systems ?
Semantics-related tasks
They are difficult (partly) because that
the involved data are ambiguous
Sign up today - FREE
Mendeley saves you time finding and organizing research. Learn more
- All your research in one place
- Add and import papers easily
- Access it anywhere, anytime