Output-associative RVM regression for dimensional and continuous emotion prediction

@article{Nicolaou2011OutputassociativeRR,
  title={Output-associative RVM regression for dimensional and continuous emotion prediction},
  author={Mihalis A. Nicolaou and Hatice Gunes and Maja Pantic},
  journal={Face and Gesture 2011},
  year={2011},
  pages={16-23}
}
Many problems in machine learning and computer vision consist of predicting multi-dimensional output vectors given a specific set of input features. In many of these problems, there exist inherent temporal and spacial dependencies between the output vectors, as well as repeating output patterns and input-output associations, that can provide more robust and accurate predictors when modelled properly. With this intrinsic motivation, we propose a novel Output-Associative Relevance Vector Machine… CONTINUE READING

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References

Publications referenced by this paper.
SHOWING 1-10 OF 22 REFERENCES

Structured output-associative regression

  • 2009 IEEE Conference on Computer Vision and Pattern Recognition
  • 2009
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Twin Gaussian Processes for Structured Prediction

  • International Journal of Computer Vision
  • 2008
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

A survey of affect recognition methods: Audio, visual, and spontaneous expressions

Z. Zeng
  • IEEE Tran. on Pattern Analysis and Machine Intelligence, vol. 31, pp. 39–58, 2009.
  • 2009
VIEW 2 EXCERPTS

Neural correlates of processing valence and arousal in affective words

P. A. Lewis
  • Cerebral Cortex, vol. 17, no. 3, pp. 742–748, Mar 2007.
  • 2007
VIEW 1 EXCERPT