Conformal prediction for reliable machine learning theory, adaptations, and applications / [edited by] Vineeth Balasubramanian, Shen-Shyang Ho, Vladimir Vovk.

Other author Balasubramanian, Vineeth.
Other author Ho, Shen-Shyang.
Other author Vovk, Vladimir, 1960-
Format Electronic
Publication InfoAmsterdam ; Boston : Elsevier/Morgan Kaufmann, [2014]
Descriptionxxiii, 298 pages : illustrations ; 24 cm
Supplemental ContentFull text available from eBook - Computer Science 2014 [EBCCS14]
Subjects
Machine learning.

Contents Machine generated contents note: Section I: Theory 1: The Basic Conformal Prediction Framework 2: Beyond the Basic Conformal Prediction Framework Section II: Adaptations 3: Active Learning using Conformal Prediction 4: Anomaly Detection 5: Online Change Detection by Testing Exchangeability 6. Feature Selection and Conformal Predictors 7. Model Selection 8. Quality Assessment 9. Other Adaptations Section III: Applications 10. Biometrics 11. Diagnostics and Prognostics by Conformal Predictors 12. Biomedical Applications using Conformal Predictors 13. Reliable Network Traffic Classification and Demand Prediction 14. Other Applications.
Abstract "Traditional, low-dimensional, small scale data have been successfully dealt with using conventional software engineering and classical statistical methods, such as discriminant analysis, neural networks, genetic algorithms and others. But the change of scale in data collection and the dimensionality of modern data sets has profound implications on the type of analysis that can be done. Recently several kernel-based machine learning algorithms have been developed for dealing with high-dimensional problems, where a large number of features could cause a combinatorial explosion. These methods are quickly gaining popularity, and it is widely believed that they will help to meet the challenge of analysing very large data sets. Learning machines often perform well in a wide range of applications and have nice theoretical properties without requiring any parametric statistical assumption about the source of data (unlike traditional statistical techniques). However, a typical drawback of many machine learning algorithms is that they usually do not provide any useful measure of con dence in the predicted labels of new, unclassi ed examples. Con dence estimation is a well-studied area of both parametric and non-parametric statistics; however, usually only low-dimensional problems are considered"-- Provided by publisher.
Bibliography noteIncludes bibliographical references (pages 273-293) and index.
Access restrictionAvailable only to authorized users.
Technical detailsMode of access: World Wide Web
Genre/formElectronic books.
LCCN 2014003894
ISBN9780123985378 (paperback)

Availability

Library Location Call Number Status Item Actions
Electronic Resources Access Content Online ✔ Available