ML Anthology

Large-Scale Multiclass Transduction

Thomas Gärtner, Quoc V. Le, Simon Burton, Alex J. Smola, Vishy Vishwanathan
NeurIPS 2005 pp. 411-418
/neurips/2005/gartner2005neurips-largescale/

Abstract

We present a method for performing transductive inference on very large datasets. Our algorithm is based on multiclass Gaussian processes and is effective whenever the multiplication of the kernel matrix or its inverse with a vector can be computed sufficiently fast. This holds, for instance, for certain graph and string kernels. Transduction is achieved by varia- tional inference over the unlabeled data subject to a balancing constraint.

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Text

Gärtner et al. "Large-Scale Multiclass Transduction." Neural Information Processing Systems, 2005.

Markdown

[Gärtner et al. "Large-Scale Multiclass Transduction." Neural Information Processing Systems, 2005.](https://mlanthology.org/neurips/2005/gartner2005neurips-largescale/)

BibTeX

@inproceedings{gartner2005neurips-largescale,
  title     = {{Large-Scale Multiclass Transduction}},
  author    = {Gärtner, Thomas and Le, Quoc V. and Burton, Simon and Smola, Alex J. and Vishwanathan, Vishy},
  booktitle = {Neural Information Processing Systems},
  year      = {2005},
  pages     = {411-418},
  url       = {https://mlanthology.org/neurips/2005/gartner2005neurips-largescale/}
}