>VITAE:
John Shawe-Taylor, professor at University of Southampton, leader of the ISIS research group, director of the Centre for Computational Statistics and Machine Learning at University College, London. Coordinator of the PASCAL Network of Excellence in Pattern Analysis, Statistical Modelling and Computational Learning (Framework VI) involving 56 partners. He has coordinated a number of European wide projects investigating the theory and practice of Machine Learning, including the NeuroCOLT projects. He is co-author of the books 'Introduction to Support Vector Machines', the first comprehensive account of this new generation of machine learning algorithms, and of 'Kernel Methods for Pattern Analysis'. John Shawe-Taylor has made significant contributions to statistical learning theory --- including the analysis of Support Vector Machines, Boosting and Kernel Principal Components Analysis --- and has shown the viability of applying these techniques to document analysis and computer vision. He has published over 150 research papers.

The paper introduces a new framework for learning probability density functions. A theoretical analysis suggests that we can tailor a distribution for a class of tasks by training it to fit a small subsample. Experimental evidence is given to support the theoretical analysis.