[PAST] Density Ratio Estimation in Machine Learning
[PAST] Density Ratio Estimation in Machine Learning 
2013 / 02 / 22
Location: Room 309, Building 302
Speaker: Masashi Sugiyama
- Bio :
Masashi Sugiyama is currently an associate professor of engineering in computer science at Tokyo Institute of Technology. His research interests are machine learning both in theories and in applications, and he has many experiences in treating signal processing, image processing and robot data. He received the Faculty Award from IBM for the research on learning under non-stationarity condition, and he received the Nagao Special Researcher Award from IPSJ for his contribution to the density-ratio paradigm for the information estimation problem.
- Abstract :
In statistical machine learning, avoiding density estimation is essential because it is often more difficult than solving a target machine learning problem itself. This is often referred to as Vapnik's principle, and the support vector machine is one of the successful realizations of this principle. Following this spirit, a new machine learning framework based on the ratio of probability density functions has been introduced. This density-ratio framework includes various important machine learning tasks such as transfer learning, outlier detection, feature selection, clustering, and conditional density estimation. All these tasks can be effectively
and efficiently solved in a unified manner by direct estimating the density ratio without going through density estimation. In this talk, I give an overview of theory, algorithms, and application of density ratio estimation.