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.
Bibliographic reference. Karsmakers, Peter / Pelckmans, Kristiaan / Suykens, Johan / hamme, Hugo Van (2007): "Fixed-size kernel logistic regression for phoneme classification", In INTERSPEECH-2007, 78-81.