アブストラクト
| Date | 2:00pm Sep 19, 2019 |
| Speaker | Hiroto Saigo Associate Professor, Faculty of Information Science and Electrical Engineering, Kyushu University |
| Title | Prediction models that consider variable interactions |
| Abstract | 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. |