@inproceedings{456e5c1d2c68493180b25bc5dcfb48f7,
title = "BLPA: Bayesian learn-predict-adjust method for online detection of recurrent changepoints",
abstract = "Online changepoint detection is an important task for machine learning in changing environments, as it signals when the learning model needs to be updated. Presence of noise that can be mistaken for real changes makes it difficult to develop an effective approach that would have a low false alarm rate and being able to detect all the changes with a minimal delay. In this paper we study how performance of popular Bayesian online detectors can be improved in case of recurrent changes. Modelling recurrence allows us to anticipate future changepoints and predict their locations in time. We propose an approach for inducing and integrating recurrence information in the streaming settings, and demonstrate its effectiveness on synthetic and real-world human activity datasets.",
keywords = "Detectors, Bayes methods, Hazards, Mathematical model, Electronic mail, Data models, Noise measurement",
author = "Alexandr Maslov and Mykola Pechenizkiy and Yulong Pel and Indre Zliobaite and Alexander Shklyaev and Tommi Karkkainen and Jaakko Hollmen",
year = "2017",
month = jun,
day = "30",
doi = "10.1109/IJCNN.2017.7966085",
language = "English",
series = "Proceedings of International Joint Conference on Neural Networks",
publisher = "IEEE",
pages = "1916--1923",
booktitle = "2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings",
address = "United States",
note = "International Joint Conference on Neural Networks, IJCNN ; Conference date: 14-05-2017 Through 19-05-2017",
}