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C. Watkins (2000)

# Dynamic Alignment Kernels

In: Advances in Large Margin Classifiers, ed. by A.J. Smola and P.L. Bartlett and B. Schölkopf and D. Schuurmans, pp. 39-50, Cambridge, MA, MIT Press.

A new concept using generative models to construct Dynamic Alignment Kernels is presented. These are based on the observation that the sum of products of conditional probabilities $\sum_c p(x|c) p(x'|c)$ is a valid SV kernel. This is particularly well suited for the use of Hidden Markov Models, thereby opening the door to a large class of applications like DNA analysis or speech recognition.