Showing papers by "Marco Cuturi published in 2004"

Semigroup Kernels on Finite Sets

Abstract: Complex objects can often be conveniently represented by finite sets of simpler components, such as images by sets of patches or texts by bags of words. We study the class of positive definite (p.d.) kernels for two such objects that can be expressed as a function of the merger of their respective sets of components. We prove a general integral representation of such kernels and present two particular examples. One of them leads to a kernel for sets of points living in a space endowed itself with a positive definite kernel. We provide experimental results on a benchmark experiment of handwritten digits image classification which illustrate the validity of the approach.

A mutual information kernel for sequences

Abstract: We propose a new kernel for strings which borrows ideas and techniques from information theory and data compression. This kernel can be used in combination with any kernel method, in particular support vector machines for protein classification. By incorporating prior assumptions on the properties of the alphabet and using a Bayesian averaging framework, we compute the value of this kernel in linear time and space, benefiting from previous achievements proposed in the field of universal coding. Encouraging classification results are reported on a standard protein homology detection experiment.