Jason P. Rhinelander

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Kernel machines have gained much popularity in applications of machine learning. Support vector machines (SVMs) are a subset of kernel machines and generalize well for classification, regression, and anomaly detection tasks. The training procedure for traditional SVMs involves solving a quadratic programming (QP) problem. The QP problem scales super(More)
The use of support vector (SV) methods has been successful in many areas involving pattern recognition. Video surveillance requires pattern recognition algorithms that are efficient in their operation, and requires the use of online processing for the detection and identification of events, objects, and behaviours. To successfully use SV methods in video(More)
Kernel machines have been successfully applied to many engineering problems requiring pattern recognition and regression. Kernel machines are a family of machine learning algorithms including support vector machines (SVM) [1], kernel least mean squares adaptive filter (KLMS) [2], and kernel recursive least squares (KRLS) adaptive filter [3] to name a few.(More)
Side-scan sonar technology has been used over the last three decades for underwater surveying and imaging. Application areas of side-scan sonar include archaeology, security and defence, seabed classification, and environmental surveying. In recent years the use of autonomous underwater systems has allowed for automatic collection of data. Along with(More)
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