# A kernel-based nonparametric test for anomaly detection over line networks

@article{Zou2014AKN, title={A kernel-based nonparametric test for anomaly detection over line networks}, author={Shaofeng Zou and Yingbin Liang and H. Vincent Poor}, journal={2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)}, year={2014}, pages={1-6} }

The nonparametric problem of detecting existence of an anomalous interval over a one-dimensional line network is studied. Nodes corresponding to an anomalous interval (if one exists) receive samples generated by a distribution q, which is different from the distribution p that generates samples for other nodes. If an anomalous interval does not exist, then all nodes receive samples generated by p. It is assumed that the distributions p and q are arbitrary, and are unknown. In order to detect… CONTINUE READING

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