||2:00pm Sep 19, 2019
|| Hiroto Saigo
Faculty of Information Science and Electrical Engineering, Kyushu University
Prediction models that consider variable interactions
With the rapid increase in the availability of large amount of data,
prediction is becoming increasingly popular, and has widespread
through our daily life.
However, powerful non-linear prediction methods such as neural
networks and SVM suffer from interpretability problem, making it hard
to use in domains where the reason for decision making is required.
In this talk, we review recent advantages in the development of
prediction models that consider variable interactions, with a special
focus on interpretable parametric models. It has attractive
applications in bioinformatics such as identification of interactions
among biomarkers. |