MARC details
| 001 - CONTROL NUMBER |
|---|
| control field |
18114038 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20200226113817.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
140412s2014 ne a b 001 0 eng |
| 010 ## - LIBRARY OF CONGRESS CONTROL NUMBER |
|---|
| LC control number |
2014003894 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9780123985378 (paperback) |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
DLC |
| Language of cataloging |
eng |
| Transcribing agency |
DLC |
| Description conventions |
rda |
| Modifying agency |
LNU |
| 042 ## - AUTHENTICATION CODE |
|---|
| Authentication code |
pcc |
| 050 00 - LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
Q325.5 |
| Item number |
C668 2014 |
| 082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
006.31 |
| Edition number |
23 |
| 245 00 - TITLE STATEMENT |
|---|
| Title |
Conformal prediction for reliable machine learning : |
| Remainder of title |
theory, adaptations, and applications / |
| Statement of responsibility, etc. |
[edited by] Vineeth Balasubramanian, Shen-Shyang Ho, Vladimir Vovk. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Amsterdam ; |
| -- |
Boston : |
| Name of producer, publisher, distributor, manufacturer |
Elsevier/Morgan Kaufmann, |
| Date of production, publication, distribution, manufacture, or copyright notice |
[2014] |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xxiii, 298 p : |
| Other physical details |
ill ; |
| Dimensions |
24 cm |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Includes bibliographical references (pages 273-293) and index. |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
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. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
"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"-- |
| Assigning source |
Provided by publisher. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Machine learning. |
| 9 (RLIN) |
35 |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Artificial intelligence |
| 9 (RLIN) |
36 |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Balasubramanian, Vineeth, |
| Relator term |
editor of compilation. |
| 9 (RLIN) |
37 |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Ho, Shen-Shyang, |
| Relator term |
editor of compilation. |
| 9 (RLIN) |
38 |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Vovk, Vladimir, |
| Dates associated with a name |
1960- |
| Relator term |
editor of compilation. |
| 9 (RLIN) |
39 |
| 906 ## - LOCAL DATA ELEMENT F, LDF (RLIN) |
|---|
| a |
7 |
| b |
cbc |
| c |
orignew |
| d |
1 |
| e |
ecip |
| f |
20 |
| g |
y-gencatlg |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Books |