The Mathematics Of Generalization / Edition 1

by David. H Wolpert

Overview

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.

Product Details

ISBN-13:	9780367320515
Publisher:	Taylor & Francis
Publication date:	05/07/2019
Pages:	460
Product dimensions:	6.00(w) x 9.00(h) x (d)

About the Author

Bob Latford served as chief statistician for CBS NASCAR coverage from 1979-2000. He developed the Championship points system currently used at all Winston Cup events. He lived in Concord, North Carolina.

Table of Contents

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