Fixed-size kernel logistic regression for phoneme classification
نویسندگان
چکیده
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.
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