| Description |
1 online resource (1 volume) : illustrations |
| Contents |
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 |
|
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 |
|
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 |
|
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 |
|
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 |
| Summary |
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 |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Print version record |
| Subject |
Machine learning.
|
|
Machine learning
|
| Form |
Electronic book
|
| Author |
Balasubramanian, Vineeth.
|
|
Ho, Shen-Shyang.
|
|
Vovk, Vladimir, 1960-
|
| ISBN |
9780124017153 |
|
0124017150 |
|
0123985374 |
|
9780123985378 |
|
