Neurocomputing: foundations of research
The nature of statistical learning theory
The nature of statistical learning theory
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
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 Hierarchical Graph Neuron Scheme for Real-Time Pattern Recognition
IEEE Transactions on Neural Networks
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Many of the existing event detection schemes in wireless sensor networks that employ classification or clustering approaches suffer from high communication and computational overheads. We propose a low-computation, distributed, and lightweight event detection scheme in wireless sensor networks, which is adopted from the pattern recognition scheme known as Distributed Hierarchical Graph Neuron. The experimental results show that the proposed scheme guarantees satisfactory classification accuracy, in comparison to Support Vector Machine and Self-Organizing Map algorithms.