Selected Talks


The three pillars of machine learning (BU)

Feature vector denoising with prior network structure (New England Statistics Symposium)

Learning good representations for learning (NECSI, MIT)

Machine methods for identifying DNA binding sites, (ICMLA, Cincinnati)

Learning methods for DNA binding in computational biology, (IJCNN, Orlando)

Microarray Data Analysis (NECSI – Powerpoint)

A continuous complexity analysis of support vector machines (FOCM, Santander)

Predictive genomics, biology, medicine (Beyond Genomics)

Can neural network theory and statistical learning theory be formulated in terms of continuous complexity theory?  (Dagstuhl, Germany)

Maximum a posteriori methods for machine learning (Innsbruck, Austria)

Neural networks and radial basis functions (Bialowieza summer school)

Tutorial on Wavelets