Weighted automata kernels - General framework and algorithms

TLDR: This paper introduced a general kernel framework based on weighted transducers, rational kernels, and presented a constructive algorithm for ensuring that rational kernels are positive definite symmetric, a property which guarantees the convergence of discriminant classification algorithms such as Support Vector Machines.

Abstract: Kernel methods have found in recent years wide use in statistical learning techniques due to their good performance and their computational efficiency in high-dimensional feature space. However, text or speech data cannot always be represented by the fixed-length vectors that the traditional kernels handle. We recently introduced a general kernel framework based on weighted transducers, rational kernels ,t o extend kernel methods to the analysis of variable-length sequences and weighted automata [5] and described their application to spoken-dialog applications. We presented a constructive algorithm for ensuring that rational kernels are positive definite symmetric, a property which guarantees the convergence of discriminant classification algorithms such as Support Vector Machines, and showed that many string kernels previously introduced in the computational biology literature are special instances of such positive definite symmetric rational kernels [4]. This paper reviews the essential results given in [5, 3, 4] and presents them in the form of a short tutorial.