Ratio semi-definite classifiers

Jonathan Malkin, Jeff Bilmes
2008 Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing  
We present a novel classification model that is formulated as a ratio of semi-definite polynomials. We derive an efficient learning algorithm for this classifier, and apply it to two separate phoneme classification corpora. Results show that our disciminatively trained model can achieve accuracies comparable with state-of-the-art techniques such as multi-layer perceptrons, but does not posses the overconfident bias often found in models based on ratios of exponentials.
doi:10.1109/icassp.2008.4518559 dblp:conf/icassp/MalkinB08 fatcat:xjtj2t3nt5fmfedyx4iqgfynzu