Multi-layer ratio Semi-Definite Classifiers

  • Authors:

  • Jonathan Malkin;Jeff Bilmes

  • Affiliations:

  • Department of Electrical Engineering, University of Washington, Seattle, Washington, USA;Department of Electrical Engineering, University of Washington, Seattle, Washington, USA

  • Venue:
  • ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
  • Year:
  • 2009

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Abstract

We develop a novel extension to the Ratio Semi-definite Classifier, a discriminative model formulated as a ratio of semi-definite polynomials. By adding a hidden layer to the model, we can efficiently train the model, while achieving higher accuracy than the original version. Results on artificial 2-D data as well as two separate phone classification corpora show that our multi-layer model still avoids the overconfidence bias found in models based on ratios of exponentials, while remaining competitive with state-of-the-art techniques such as multi-layer perceptrons.