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Feature saliency estimation and feature selection are important tasks in machine learning applications. Filters, such as distance measures are commonly used as an efficient means of estimating the saliency of individual features. However, feature rankings derived from different distance measures are frequently inconsistent. This can present reliability(More)
Seabed pockmarks are of great interest to geologists and the marine geotechnical community. Identifying and mapping pockmarks rendered in multi-beam bathymetry data is an important but expensive manual process. In this paper, a new Machine Learning approach to automating the task is presented. Useful, low-dimensional feature vectors yielding very good(More)
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