Competitive Online Generalised Linear Regression with Multidimensional Outputs

Raisa Dzhamtyrova, Yury Kalnishkan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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We apply online prediction with expert advice to construct a universal algorithm for multi-class classification problem. Our experts are generalised linear regression models with multidimensional outputs, i.e. neural networks with multiple output nodes but no hidden nodes. We allow the final layer transfer function to be a softmax function with linear activations to all output neurons. We build an online algorithm competitive with all the experts of relevant models of this type and derive an upper bound on the cumulative loss of the algorithm. We carry out experiments on three data sets and compare cumulative losses of our algorithm and a single neuron with multiple output nodes.
Original languageEnglish
Title of host publicationProceedings of IJCNN 2019
Number of pages8
ISBN (Electronic)978-1-7281-1985-4
ISBN (Print)978-1-7281-1986-1
Publication statusPublished - 2019


  • on-line learning
  • regression
  • kullback-leibler distance
  • competitive prediction

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