Statistical analysis with missing data
Statistical analysis with missing data
C4.5: programs for machine learning
C4.5: programs for machine learning
Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
Inference in Hybrid Networks: Theoretical Limits and Practical Algorithms
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Variational Learning for Switching State-Space Models
Neural Computation
Factorial Switching Linear Dynamical Systems Applied to Physiological Condition Monitoring
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Condition monitoring of premature babies in intensive care can be carried out using a Factorial Switching Linear Dynamical System (FSLDS) [15]. A crucial part of training the FSLDS is the manual calibration stage, where an interval of normality must be identified for each baby that is monitored. In this paper we replace this manual step by using a classifier to predict whether an interval is normal or not. We show that the monitoring results obtained using automated calibration are almost as good as those using manual calibration.