We present an application of kernel methods to extracting relations from unstructured natural language sources.

Editor: John Shawe-Taylor We study the problem of learning many related tasks simultaneously using kernel methods and regularization. The standard single-task kernel methods, such as support vector machines and regularization networks, are extended to the case of multi-task learning. Our analysis

1 A primer on kernel methods

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The term kernel is derived from a word that can be traced back to c. 1000 and originally meant a seed (contained within a fruit) or the softer (usually edible) part contained within the hard shell of a nut or stone-fruit. The former meaning is now obsolete. It was first used in mathematics when

Abstract In this paper, we introduce a framework for regularized least-squares (RLS) type of ranking cost functions and we propose three such cost functions. Further, we propose a kernel-based preference learning algorithm, which we call RankRLS, for minimizing these functions. It is shown

We present methods for dealing with missing variables in the context of Gaussian Processes and Support Vector Machines. This solves an important problem which has largely been ig-nored by kernel methods: How to systemati-cally deal with incomplete data? Our method can also be applied to problems

Kernel methods have gained a great deal of popularity in the machine learning commu-nity as a method to learn indirectly in high-dimensional feature spaces. Those interested in relational learning have recently begun to cast learning from structured and relational data in terms of kernel operations

properties as measures of statistical dependence. On the other hand, building on recent developments in kernel methods, we show that these criteria can be computed efficiently. Minimizing these criteria leads to flexible and robust algorithms for ICA. We illustrate with simulations involving a wide variety

We introduce a new method of constructing kernels on sets whose elements are discrete structures like strings, trees and graphs. The method can be applied iteratively to build a kernel on an infinite set from kernels involving generators of the set. The family of kernels generated generalizes