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Relative loss bounds for multidimensional regression problems

Kivinen, Jyrki; Warmuth, Manfred

Description

We study on-line generalized linear regression with multidimensional outputs, i.e., neural networks with multiple output nodes but no hidden nodes. We allow at the final layer transfer functions such as the softmax function that need to consider the linear activations to all the output neurons. The weight vectors used to produce the linear activations are represented indirectly by maintaining separate parameter vectors. We get the weight vector by applying a particular parameterization function...[Show more]

CollectionsANU Research Publications
Date published: 2001
Type: Journal article
URI: http://hdl.handle.net/1885/69960
Source: Machine Learning
DOI: 10.1023/A:1017938623079

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