Conformal prediction for reliable machine learning : theory, adaptations, and applications / (Record no. 246908)
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| fixed length control field | 06326cam a2200565Ia 4500 |
| 001 - CONTROL NUMBER | |
| control field | ocn878922864 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OCoLC |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20190328114807.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS | |
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| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION | |
| fixed length control field | cr cnu---unuuu |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 140502s2014 ne ob 001 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | IDEBK |
| Language of cataloging | eng |
| Description conventions | pn |
| Transcribing agency | IDEBK |
| Modifying agency | N$T |
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| 019 ## - | |
| -- | 1066018126 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9780124017153 |
| Qualifying information | (electronic bk.) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 0124017150 |
| Qualifying information | (electronic bk.) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 1306697484 |
| Qualifying information | (electronic bk.) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781306697484 |
| Qualifying information | (electronic bk.) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| Canceled/invalid ISBN | 9780123985378 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| Canceled/invalid ISBN | 0123985374 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | (OCoLC)878922864 |
| Canceled/invalid control number | (OCoLC)1066018126 |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | Q325.5 |
| Item number | C668 2014eb |
| 060 #4 - NATIONAL LIBRARY OF MEDICINE CALL NUMBER | |
| Classification number | Online Book |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | COM |
| Subject category code subdivision | 000000 |
| Source | bisacsh |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.3/1 |
| Edition number | 23 |
| 245 00 - TITLE STATEMENT | |
| Title | Conformal prediction for reliable machine learning : theory, adaptations, and applications / |
| Medium | [electronic resource] |
| Statement of responsibility, etc. | edited by Vineeth Balasubramanian, Shen-Shyang Ho, Vladimir Vovk. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc. | Amsterdam ; |
| -- | Boston : |
| Name of publisher, distributor, etc. | Morgan Kaufmann, |
| Date of publication, distribution, etc. | �2014. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 1 online resource |
| 336 ## - CONTENT TYPE | |
| Content type term | text |
| Content type code | txt |
| Source | rdacontent |
| 337 ## - MEDIA TYPE | |
| Media type term | computer |
| Media type code | c |
| Source | rdamedia |
| 338 ## - CARRIER TYPE | |
| Carrier type term | online resource |
| Carrier type code | cr |
| Source | rdacarrier |
| 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 confidence in the predicted labels of new, unclassifed examples. Confidence 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. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc | Includes bibliographical references and index. |
| 588 0# - SOURCE OF DESCRIPTION NOTE | |
| Source of description note | Print version record. |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Half Title; Title Page; Copyright; Copyright Permissions; Contents; Contributing Authors; Foreword; Preface; Book Organization; Part I: Theory; Part II: Adaptations; Part III: Applications; Companion Website; Contacting Us; Acknowledgments; Part I: Theory; 1 The Basic Conformal Prediction Framework; 1.1 The Basic Setting and Assumptions; 1.2 Set and Confidence Predictors; 1.2.1 Validity and Efficiency of Set and Confidence Predictors; 1.3 Conformal Prediction; 1.3.1 The Binary Case; 1.3.2 The Gaussian Case; 1.4 Efficiency in the Case of Prediction without Objects. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 1.5 Universality of Conformal Predictors1.6 Structured Case and Classification; 1.7 Regression; 1.8 Additional Properties of Validity and Efficiency in the Online Framework; 1.8.1 Asymptotically Efficient Conformal Predictors; Acknowledgments; 2 Beyond the Basic Conformal Prediction Framework; 2.1 Conditional Validity; 2.2 Conditional Conformal Predictors; 2.2.1 Venn's Dilemma; 2.3 Inductive Conformal Predictors; 2.3.1 Conditional Inductive Conformal Predictors; 2.4 Training Conditional Validity of Inductive Conformal Predictors; 2.5 Classical Tolerance Regions. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 2.6 Object Conditional Validity and Efficiency2.6.1 Negative Result; 2.6.2 Positive Results; 2.7 Label Conditional Validity and ROC Curves; 2.8 Venn Predictors; 2.8.1 Inductive Venn Predictors; 2.8.2 Venn Prediction without Objects; Acknowledgments; Part II: Adaptations; 3 Active Learning; 3.1 Introduction; 3.2 Background and Related Work; 3.2.1 Pool-based Active Learning with Serial Query; SVM-based methods; Statistical methods; Ensemble-based methods; Other methods; 3.2.2 Batch Mode Active Learning; 3.2.3 Online Active Learning; 3.3 Active Learning Using Conformal Prediction. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 3.3.1 Query by Transduction (QBT)Algorithmic formulation; 3.3.2 Generalized Query by Transduction; Algorithmic formulation; Combining multiple criteria in GQBT; 3.3.3 Multicriteria Extension to QBT; 3.4 Experimental Results; 3.4.1 Benchmark Datasets; 3.4.2 Application to Face Recognition; 3.4.3 Multicriteria Extension to QBT; 3.5 Discussion and Conclusions; Acknowledgments; 4 Anomaly Detection; 4.1 Introduction; 4.2 Background; 4.3 Conformal Prediction for Multiclass Anomaly Detection; 4.3.1 A Nonconformity Measure for Multiclass Anomaly Detection; 4.4 Conformal Anomaly Detection. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 4.4.1 Conformal Anomalies4.4.2 Offline versus Online Conformal Anomaly Detection; 4.4.3 Unsupervised and Semi-supervised Conformal Anomaly Detection; 4.4.4 Classification Performance and Tuning of the Anomaly Threshold; 4.5 Inductive Conformal Anomaly Detection; 4.5.1 Offline and Semi-Offline Inductive Conformal Anomaly Detection; 4.5.2 Online Inductive Conformal Anomaly Detection; 4.6 Nonconformity Measures for Examples Represented as Sets of Points; 4.6.1 The Directed Hausdorff Distance; 4.6.2 The Directed Hausdorff k-Nearest Neighbors Nonconformity Measure. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Machine learning. |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | COMPUTERS |
| General subdivision | General. |
| Source of heading or term | bisacsh |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Machine learning. |
| Source of heading or term | fast |
| Authority record control number | (OCoLC)fst01004795 |
| 655 #4 - INDEX TERM--GENRE/FORM | |
| Genre/form data or focus term | Electronic books. |
| 655 #4 - INDEX TERM--GENRE/FORM | |
| Genre/form data or focus term | Llibres electr�onics. |
| 655 #0 - INDEX TERM--GENRE/FORM | |
| Genre/form data or focus term | Electronic book. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Balasubramanian, Vineeth, |
| Relator term | editor. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Ho, Shen-Shyang, |
| Relator term | editor. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Vovk, Vladimir, |
| Dates associated with a name | 1960- |
| Relator term | editor. |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Relationship information | Print version: |
| Title | Conformal prediction for reliable machine learning. |
| Place, publisher, and date of publication | Amsterdam ; Boston : Morgan Kaufmann, 2014 |
| International Standard Book Number | 9780123985378 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Materials specified | ScienceDirect |
| Uniform Resource Identifier | http://www.sciencedirect.com/science/book/9780123985378 |
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