Accurate and Scalable Prediction of Multi-relational data
Finished by March 31, 2014
The University of Tokyo, Graduate School of Information Science and Technology, Associate Professor
Phenomena in the real world are often represented as relationships among various entities, but most of the existing data analysis approaches are limited to handling each entity independently. In this project, we develop scalable and accurate machine learning methods to predict complex relationships among data, and apply them to various important real-world problems.
