Kernel supervised learning methods can be unified by utilizing the tools from regularization theory. The duality between regularization and prior leads to interpreting regularizat...
Motivation: Remote homology detection between protein sequences is a central problem in computational biology. Supervised learning algorithms based on support vector machines are ...
A minimally supervised machine learning framework is described for extracting relations of various complexity. Bootstrapping starts from a small set of n-ary relation instances as...
This paper introduces an approach for identifying predictive structures in relational data using the multiple-instance framework. By a predictive structure, we mean a structure th...
Abstract Identifier attributes--very high-dimensional categorical attributes such as particular product ids or people's names--rarely are incorporated in statistical modeling....