This book provides different mathematical frameworks for addressing supervised learning. It is based on a workshop held under the auspices of the Center for Nonlinear Studies at Los Alamos and the Santa Fe Institute in the summer of 1992.

About the Santa Fe Institute -- Santa Fe Institute Studies in the Sciences of Complexity -- Preface -- The Status of Supervised Learning Science Circa 1994: The Search for a Consensus -- Reflections After Refereeing Papers for NIPS -- The Probably Approximately Correct (PAC) and Other Learning Models -- Decision Theoretic Generalizations of the PAC Model for Neural Net and Other Learning Applications -- The Relationship Between PAC, the Statistical Physics Framework, the Bayesian Framework, and the VC Framework -- Statistical Physics Models of Supervised Learning -- On Exhaustive Learning -- A Study of Maximal-Coverage Learning Algorithms -- On Bayesian Model Selection -- Soft Classification, a.k.a. Risk Estimation, via Penalized Log Likelihood and Smoothing Spline Analysis of Variance -- Current Research -- Preface to "Simplifying Neural Networks by Soft Weight Sharing" -- Simplifying Neural Networks by Soft Weight Sharing -- Error-Correcting Output Codes: A General Method for Improving Multiclass Inductive Learning Programs -- Image Segmentation and Recognition