Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A model for reasoning about persistence and causation
Computational Intelligence
Dynamic network models for forecasting
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Machine Learning - Special issue on learning with probabilistic representations
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Hidden Markov models for online classification of single trial EEG data
Pattern Recognition Letters
Handbook of Temporal Reasoning in Artificial Intelligence (Foundations of Artificial Intelligence (Elsevier))
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Dynamic Bayesian Networks for Real-Time Classification of Seismic Signals
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Continuous Time Bayesian Networks for Host Level Network Intrusion Detection
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Artificial Intelligence in Medicine
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Temporal Bayesian classifiers for modelling muscular dystrophy expression data
Intelligent Data Analysis - Selected papers from IDA2005, Madrid, Spain
A spatio-temporal Bayesian network classifier for understanding visual field deterioration
Artificial Intelligence in Medicine
Learning continuous-time social network dynamics
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Temporal Information Systems in Medicine
Temporal Information Systems in Medicine
Importance Sampling for Continuous Time Bayesian Networks
The Journal of Machine Learning Research
Facial event classification with task oriented dynamic Bayesian network
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Continuous time bayesian networks
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Forecasting sleep apnea with dynamic network models
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
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The class of continuous time Bayesian network classifiers is defined; it solves the problem of supervised classification on multivariate trajectories evolving in continuous time. The trajectory consists of the values of discrete attributes that are measured in continuous time, while the predicted class is expected to occur in the future. Two instances from this class, namely the continuous time naive Bayes classifier and the continuous time tree augmented naive Bayes classifier, are introduced and analyzed. They implement a trade-off between computational complexity and classification accuracy. Learning and inference for the class of continuous time Bayesian network classifiers are addressed, in the case where complete data are available. A learning algorithm for the continuous time naive Bayes classifier and an exact inference algorithm for the class of continuous time Bayesian network classifiers are described. The performance of the continuous time naive Bayes classifier is assessed in the case where real-time feedback to neurological patients undergoing motor rehabilitation must be provided.