著者

熊谷 充敏 岩田 具治

出版者

一般社団法人 人工知能学会

雑誌

人工知能学会論文誌 (ISSN:13460714)

巻号頁・発行日

vol.33, no.2, pp.D-H92_1-9, 2018-03-01 (Released:2018-03-01)

参考文献数

33

---

We propose probabilistic models for predicting future classifiers given labeled data with timestamps collected until the current time. In some applications, the decision boundary changes over time. For example, in activity recognition using sensor data, the decision boundary can vary since user activity patterns dynamically change. Existing methods require additional labeled and/or unlabeled data to learn a time-evolving decision boundary. However, collecting these data can be expensive or impossible. By incorporating time-series models to capture the dynamics of a decision boundary, the proposed model can predict future classifiers without additional data. We developed two learning algorithms for the proposed model on the basis of variational Bayesian inference. The effectiveness of the proposed method is demonstrated with experiments using synthetic and real-world data sets.