Cargando…
Conformal prediction for reliable machine learning : theory, adaptations and applications /
The conformal predictions framework is a recent development in machine learning that can associate a reliable measure of confidence with a prediction in any real-world pattern recognition application, including risk-sensitive applications such as medical diagnosis, face recognition, and financial ri...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Waltham, MA :
Morgan Kaufmann,
2014.
|
| Edición: | 1st ed. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
| LEADER | 00000cam a2200000Ia 4500 | ||
|---|---|---|---|
| 001 | OR_ocn880945071 | ||
| 003 | OCoLC | ||
| 005 | 20231017213018.0 | ||
| 006 | m o d | ||
| 007 | cr unu|||||||| | ||
| 008 | 140604s2014 maua ob 001 0 eng d | ||
| 040 | |a UMI |b eng |e pn |c UMI |d DEBBG |d DEBSZ |d EBLCP |d OCLCQ |d OCLCF |d OCLCQ |d CEF |d AU@ |d OCLCQ |d OCLCO |d OCLCQ |d OCLCO | ||
| 019 | |a 879074318 | ||
| 020 | |a 9780124017153 | ||
| 020 | |a 0124017150 | ||
| 020 | |a 0123985374 | ||
| 020 | |a 9780123985378 | ||
| 020 | |z 9780123985378 | ||
| 029 | 1 | |a DEBBG |b BV042033020 | |
| 029 | 1 | |a DEBSZ |b 41418498X | |
| 029 | 1 | |a DEBSZ |b 431678278 | |
| 029 | 1 | |a GBVCP |b 88273119X | |
| 035 | |a (OCoLC)880945071 |z (OCoLC)879074318 | ||
| 037 | |a CL0500000443 |b Safari Books Online | ||
| 050 | 4 | |a Q325.5 |b .C668 2014 | |
| 082 | 0 | 4 | |a 006.3/1 |a 006.31 |
| 049 | |a UAMI | ||
| 245 | 0 | 0 | |a Conformal prediction for reliable machine learning : |b theory, adaptations and applications / |c edited by Vineeth Balasubramanian, Shen-Shyang Ho, Vladimir Vovk. |
| 250 | |a 1st ed. | ||
| 260 | |a Waltham, MA : |b Morgan Kaufmann, |c 2014. | ||
| 300 | |a 1 online resource (1 volume) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Print version record. | |
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a 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 | |a 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 | |a 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 | |a 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 | |a 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. | |
| 520 | |a The conformal predictions framework is a recent development in machine learning that can associate a reliable measure of confidence with a prediction in any real-world pattern recognition application, including risk-sensitive applications such as medical diagnosis, face recognition, and financial risk prediction. Conformal Predictions for Reliable Machine Learning: Theory, Adaptations and Applications captures the basic theory of the framework, demonstrates how to apply it to real-world problems, and presents several adaptations, including active learning, change detection, and anomaly. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 7 | |a Machine learning |2 fast | |
| 700 | 1 | |a Balasubramanian, Vineeth. | |
| 700 | 1 | |a Ho, Shen-Shyang. | |
| 700 | 1 | |a Vovk, Vladimir, |d 1960- | |
| 776 | 0 | 8 | |i Print version: |t Conformal prediction for reliable machine learning. |d Amsterdam ; Boston : Morgan Kaufmann, 2014 |z 9780123985378 |w (DLC) 2014003894 |w (OCoLC)869777037 |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9780123985378/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 938 | |a ProQuest Ebook Central |b EBLB |n EBL1680381 | ||
| 994 | |a 92 |b IZTAP | ||