Skip navigation
Skip navigation

Relative loss bounds for multidimensional regression problems

Kivinen, Jyrki; Warmuth, Manfred


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
Source: Machine Learning
DOI: 10.1023/A:1017938623079


File Description SizeFormat Image
01_Kivinen_Relative_loss_bounds_for_2001.pdf209.8 kBAdobe PDF    Request a copy

Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.

Updated:  23 August 2018/ Responsible Officer:  University Librarian/ Page Contact:  Library Systems & Web Coordinator