Fast Learning in Multi-Resolution Hierarchies

A variety of approaches to adaptive information processing have been developed by workers in disparate disciplines. These include the large body of literature on approximation and interpolation techniques (curve and surface fitting), the linear, real-time adaptive signal processing systems (such as the adaptive linear combiner and the Kalman filter), and most recently, the reincarnation of nonlinear neural network models such as the multilayer perceptron. Each of these methods has its strengths and weaknesses. The curve and surface fitting techniques are excellent for off-line data analysis, but are typically not formulated withreal-time applications in mind. The linear techniques of adaptive signal processing and adaptive control are well-characterized, but are limited to applications forwhich linear descriptions are appropriate. Finally, neural network learning models such as back propagation have proven extremely versatile at learning a wide variety of nonlinear mappings, but tend to be very slow computationally and are not yet well characterized.