Rong Jin recipient of the Faculty Early Career Development Program (CAREER) Award

Year: 
2007

Photo of Rong JinImportant applications in science and business depend on automatic classification of large volumes of items (examples) into predefined classes. Multi-label learning refers to the classification problem where each example can be assigned to multiple class labels simultaneously. The classification problem is germane to many different domains, such as natural language processing, computer vision, human computer interaction, bioinformatics, health care, and physiology. Existing machine learning technologies are unsuitable for large-scale multi-label learning because they are unable to handle rare class classification problems and poorly distinguish classes with similar input patterns.

Click here to read more of the NSF Award Abstract.

Award Description: 
CAREER: Large-Scale Multi-label Learning
Department: 
Computer Science & Engineering