The nature of statistical learning theory
The nature of statistical learning theory
Fuzzy and Neural Approaches in Engineering
Fuzzy and Neural Approaches in Engineering
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Classes of kernels for machine learning: a statistics perspective
The Journal of Machine Learning Research
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Anomaly detection in radiation sensor data with application to transportation security
IEEE Transactions on Intelligent Transportation Systems
Neurofuzzy approaches to anticipation: a new paradigm forintelligent systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Fast processing of consecutive measurements and accurate detection of abrupt changes is of primary importance for online monitoring of radiation. In this paper, a hybrid anticipatory system is discussed together with its application to real time radiation monitoring. The system utilizes the synergism of an artificial neural network ANN with support vector machines SVM to tackle the problem of anticipating the measurements for an ahead-in-time horizon. The system employs an ensemble of support vector regressors SVRs whose predictions constitute the input values to a neural network. Therefore, it is the neural network that provides the final predictions over the designated time horizon. The neuro-SVM predictions are divided by the respective observed values and compared with a predetermined threshold where an abrupt change is identified if the ratio exceeds the threshold value. The proposed anticipatory system is compared with the naïve prediction approach as well as the independent support vector regressors. Results clearly demonstrate that the neuro-SVM outperforms the naïve predictor and the individual SVRs in the majority of the cases regarding prediction accuracy and rate of abrupt change detection.