Alexander Iversen

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0-7803-9048-2/05/$20.00 ©2005 IEEE Abstract— The Multi-Layer Perceptron (MLP) classifier has excellent discriminatory properties but forms open decision boundaries, which makes it inappropriate for detecting nonclass data. The Auto-Association Neural Network (AANN), on the other hand, creates closed decision boundaries around the training set and is thus(More)
An important element in many wireless communication activities is distinguishing between different radio signals. In this paper, we address some important problems within radio communication signal classification, one of which is the detection of unknown signal formats. To tackle some of these problems, we propose a combined classifier, consisting of two(More)
Anomaly detectors (or novelty detectors) are systems for detecting behaviour that deviates from "normality ", and are useful in a wide range of surveillance, monitoring and diagnosis applications. Feed-forward auto-associative neural networks have, in several studies, shown to be effective anomaly detectors although they have a tendency to produce false(More)
A way of achieving robustness for real-world pattern recognition problems may be through integrating neural network strategies for discrimination, recognition and clustering. Making use of the knowledge that these diverse strategies provide would require us to look at structural design, connectionist versus non-connectionist combination methodologies, and(More)
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