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Machine Learning

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A Topographic Support Vector Machine: Classification Using Local Label Configurations
Citation key Mohr2005a
Author Mohr, J. and Obermayer, K.
Title of Book Advances in Neural Information Processing Systems 17
Pages 929 – 936
Year 2005
Editor Saul L. and Weiss Y. and Bottou L.
Publisher MIT Press
Abstract The standard approach to the classification of objects is to consider the examples as independent and identically distributed (iid). In many real world settings, however, this assumption is not valid, because a topographical relationship exists between the objects. In this contribution we consider the special case of image segmentation, where the objects are pixels and where the underlying topography is a 2D regular rectangular grid. We introduce a classification method which not only uses measured vectorial feature information but also the label configuration within a topographic neighborhood. Due to the resulting dependence between the labels of neighboring pixels, a collective classification of a set of pixels becomes necessary. We propose a new method called ’Topographic Support Vector Machine’ (TSVM), which is based on a topographic kernel and a self-consistent solution to the label assignment shown to be equivalent to a recurrent neural network. The performance of the algorithm is compared to a conventional SVM on a cell image segmentation task.
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