Optimal Bayesian Classification

Lori A. Dalton, Edward R. Dougherty

ISBN 10: 1510630694 / ISBN 13: 9781510630697

Published by SPIE--The International Society for Optical Engineering, 2020

About this Item

362 pp., Paperback, new. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country.

Seller Inventory # ZB1203936

Bibliographic Details

• Title: Optimal Bayesian Classification
• Publisher: SPIE--The International Society for Optical Engineering
• Publication Date: 2020
• Language: English
• ISBN 10: 1510630694
• ISBN 13: 9781510630697
• Binding: Soft cover
• Condition: New

About this title

Synopsis

The most basic problem of engineering is the design of optimal operators. Design takes different forms depending on the random process constituting the scientific model and the operator class of interest. For classification, the random process is a feature-label distribution, and a Bayes classifier minimizes classification error. Rarely do we know the feature-label distribution or have sufficient data to estimate it.

To best use available knowledge and data, this book takes a Bayesian approach to modeling the feature-label distribution and designs an optimal classifier relative to a posterior distribution governing an uncertainty class of feature-label distributions. The origins of this approach lie in estimating classifier error when there are insufficient data to hold out test data, in which case an optimal error estimate can be obtained relative to the uncertainty class. A natural next step is to forgo classical ad hoc classifier design and find an optimal classifier relative to the posterior distribution over the uncertainty class - this being an optimal Bayesian classifier.