Fixed-size kernel logistic regression for phoneme classification

Peter Karsmakers, Kristiaan Pelckmans, Johan Suykens, Hugo Van hamme

Kernel logistic regression (KLR) is a popular non-linear classification technique. Unlike an empirical risk minimization approach such as employed by Support Vector Machines (SVMs), KLR yields probabilistic outcomes based on a maximum likelihood argument which are particularly important in speech recognition. Different from other KLR implementations we use a Nyström approximation to solve large scale problems with estimation in the primal space such as done in fixed-size Least Squares Support Vector Machines (LS-SVMs). In the speech experiments it is investigated how a natural KLR extension to multi-class classification compares to binary KLR models coupled via a one-versus-one coding scheme. Moreover, a comparison to SVMs is made.

doi: 10.21437/Interspeech.2007-33

Cite as: Karsmakers, P., Pelckmans, K., Suykens, J., Van hamme, H. (2007) Fixed-size kernel logistic regression for phoneme classification. Proc. Interspeech 2007, 78-81, doi: 10.21437/Interspeech.2007-33

@inproceedings{karsmakers07_interspeech,
  author={Peter Karsmakers and Kristiaan Pelckmans and Johan Suykens and Hugo {Van hamme}},
  title={{Fixed-size kernel logistic regression for phoneme classification}},
  year=2007,
  booktitle={Proc. Interspeech 2007},
  pages={78--81},
  doi={10.21437/Interspeech.2007-33}
}