Mar 6, 2015 · Compared to sev- eral existing anomaly detection methods, the proposed approach provides higher detection performance and excellent reasoning.
This paper presents a self-structuring technique that learns the structure of a confabulation network from unlabeled data without any assumption of the ...
Real-Time Self-Structuring Method. Recently, Chen et al. proposed a real-time self-structuring learning framework named anomaly recognition and detection (AnRAD) ...
2014. Self-structured confabulation network for fast anomaly detection and reasoning. Q Chen, Q Wu, M Bishop, R Linderman, Q Qiu. 2015 International Joint ...
This network is capable of fast incremental learning, which continuously refines the knowledge base using streaming data. Compared with several existing anomaly ...
Integrate deep face network for better news event correlation. Neuromorphic framework for real-time anomalous stream detection. Develop a self-structuring ...
The basic AnRAD system for wide area surveillance was designed with four functional areas: zone partitioning, confabulation networks, training, and detection.
People also ask
What is the difference between outlier detection and anomaly detection?
What is anomaly detection AI?
AnRAD: A Neuromorphic Anomaly Detection Framework for Massive ... Self-structured confabulation network for fast anomaly detection and reasoning.
This thesis first presents the self-structured confabulation network for anomaly detec- tion. Among the machine learning applications, unsupervised detection of ...
Self-structured confabulation network for fast anomaly detection and reasoning · Computer Science. IEEE International Joint Conference on Neural… · 2015.