From bits to information with learning machines: theory and applications

Summary form only given. Learning is becoming the central problem in trying to understand intelligence and in trying to develop intelligent machines. The paper outlines some previous efforts in developing machines that learn. It sketches the authors's work on statistical learning theory and theoretical results on the problem of classification and function approximation that connect regularization theory and support vector machines. The main application focus is classification (and regression) in various domains-such as sound, text, video and bioinformatics. In particular, the paper describe the evolution of a trainable object detection system for classifying objects-such as faces and people and cars-in complex cluttered images. Finally, it speculates on the implications of this research for how the brain works and review some data which provide a glimpse of how 3D objects are represented in the visual cortex.