The Mathematics Of Generalization

ISBN-13: 9780201409833 / Angielski / Miękka / 1995 / 460 str.

David H. Wolpert;Editors

Opis

This volume grew out of a workshop designed to bring together researchers from different fields and includes contributions from workers in Bayesian analysis, machine learning, neural nets, PAC and VC theory, classical sampling theory statistics and the statistical physics of learning. The contributions present a bird's-eye view of the subject.

Spis treści

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

O autorze

David. H Wolpert