Making large-scale support vector machine learning practical
Advances in kernel methods
Handling concept drifts in incremental learning with support vector machines
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Self-organization in ad hoc sensor networks: an empirical study
ICAL 2003 Proceedings of the eighth international conference on Artificial life
ISCC '05 Proceedings of the 10th IEEE Symposium on Computers and Communications
Single-Cycle Image Recognition Using an Adaptive Granularity Associative Memory Network
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Dynamic energy management with improved particle filter prediction in wireless sensor networks
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
One shot associative memory method for distorted pattern recognition
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
A distributed support vector machines architecture for chaotic time series prediction
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
A Weighted Voting Model of Associative Memory
IEEE Transactions on Neural Networks
A Hierarchical Graph Neuron Scheme for Real-Time Pattern Recognition
IEEE Transactions on Neural Networks
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Research trends in existing event detection schemes using Wireless Sensor Network (WSN) have mainly focused on routing and localisation of nodes for optimum coordination when retrieving sensory information. Efforts have also been put in place to create schemes that are able to provide learning mechanisms for event detection using classification or clustering approaches. These schemes entail substantial communication and computational overheads owing to the event-oblivious nature of data transmissions. In this paper, we present an event detection scheme that has the ability to distribute detection processes over the resource-constrained wireless sensor nodes and is suitable for events with spatio-temporal characteristics. We adopt a pattern recognition algorithm known as Distributed Hierarchical Graph Neuron (DHGN) with collaborative-comparison learning for detecting critical events in WSN. The scheme demonstrates good accuracy for binary classification and offers low-complexity and high-scalability in terms of its processing requirements.