Saatçi, Y and Turner, R and Rasmussen, CE (2010) Gaussian process change point models. ICML 2010 - Proceedings, 27th International Conference on Machine Learning. pp. 927-934.Full text not available from this repository.
We combine Bayesian online change point detection with Gaussian processes to create a nonparametric time series model which can handle change points. The model can be used to locate change points in an online manner; and, unlike other Bayesian online change point detection algorithms, is applicable when temporal correlations in a regime are expected. We show three variations on how to apply Gaussian processes in the change point context, each with their own advantages. We present methods to reduce the computational burden of these models and demonstrate it on several real world data sets. Copyright 2010 by the author(s)/owner(s).
|Divisions:||Div F > Computational and Biological Learning|
|Depositing User:||Cron Job|
|Date Deposited:||09 Dec 2016 17:55|
|Last Modified:||19 Jan 2017 05:55|