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Structured output-associative regression
2009
2009 IEEE Conference on Computer Vision and Pattern Recognition
Structured outputs such as multidimensional vectors or graphs are frequently encountered in real world pattern recognition applications such as computer vision, natural language processing or computational biology. This motivates the learning of functional dependencies between spaces with complex, interdependent inputs and outputs, as arising e.g. from images and their corresponding 3d scene representations. In this spirit, we propose a new structured learning method-Structured
doi:10.1109/cvprw.2009.5206699
fatcat:43xe374uezh45dvioz34t43ekm