Multi-layer ratio Semi-Definite Classifiers

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.

• Type: Conference
• Year: 2009
• Where: ICASSP
• Authors: Jonathan Malkin, Jeff Bilmes
• Keywords: Discriminative Model, ICASSP 2009, Multi-layer Model, Ratio Semi-definite Classifier, Signal Processing