Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
The nature of statistical learning theory
The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to kernel methods
Radial basis function networks 1
AI Game Programming Wisdom
Detection, Estimation, and Modulation Theory: Radar-Sonar Signal Processing and Gaussian Signals in Noise
Digital Audio Restoration: A Statistical Model Based Approach
Digital Audio Restoration: A Statistical Model Based Approach
Constructing Boosting Algorithms from SVMs: An Application to One-Class Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
One-class svms for document classification
The Journal of Machine Learning Research
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
An online kernel change detection algorithm
IEEE Transactions on Signal Processing - Part II
IEEE Transactions on Signal Processing
Speaker diarization using one-class support vector machines
Speech Communication
Discovering novelty in spatio/temporal data using one-class support vector machines
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Spatiotemporal anomaly detection in gas monitoring sensor networks
EWSN'08 Proceedings of the 5th European conference on Wireless sensor networks
Ensuring high sensor data quality through use of online outlier detection techniques
International Journal of Sensor Networks
An online core vector machine with adaptive MEB adjustment
Pattern Recognition
Rapid detection of rare geospatial events: earthquake warning applications
Proceedings of the 5th ACM international conference on Distributed event-based system
A new image-based method for event detection and extraction of noisy hydrophone data
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part II
Fast anomaly detection for streaming data
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
| Hi-index | 0.00 |
The ability to detect online abnormal events in signals is essential in many real-world signal processing applications. Previous algorithms require an explicit signal statistical model, and interpret abnormal events as statistical model abrupt changes. Corresponding implementation relies on maximum likelihood or on Bayes estimation theory with generally excellent performance. However, there are numerous cases where a robust and tractable model cannot be obtained, and model-free approaches need to be considered. In this paper, we investigate a machine learning, descriptor-based approach that does not require an explicit descriptors statistical model, based on support vector novelty detection. A sequential optimization algorithm is introduced. Theoretical considerations as well as simulations on real signals demonstrate its practical efficiency.