Conformal Prediction for Reliable Machine Learning : Theory, Adaptations and Applications

Balasubramanian, Vineeth (EDT)/ Ho, Shen-Shyang (EDT)/ Vovk, Vladimir (EDT)

Morgan Kaufmann Publishers In（2014/06発売）

• 製本 Paperback:紙装版/ペーパーバック版／ページ数 334 p.
• 言語 ENG
• 商品コード 9780123985378
• DDC分類 006.31

Full Description

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 detection. As practitioners and researchers around the world apply and adapt the framework, this edited volume brings together these bodies of work, providing a springboard for further research as well as a handbook for application in real-world problems.

Contents

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