Rational Kernels -- A General Classification Framework for the Analysis of Text and Speech

Most classification algorithms were originally designed for fixed-size vectors. However, important learning problems in natural language processing require the analysis of variable-length sequences and more generally distributions over variable-length sequences.

Rational kernels are a new family of similarity measures over variable-length sequences and their distributions. Many similarity measures commonly used in computational biology, such as the edit distance, the convolution kernels of Haussler, and other string kernels, are shown to be special cases of rational kernels.

This talk will describe general and efficient methods for computing rational kernels, and discuss some important convergence and closure properties. It will also report the results of experiments illustrating the successful use of rational kernels for several difficult prediction problems in text and speech processing.

[Joint work with Corinna Cortes and Patrick Haffner]