Conformal Prediction for Reliable Machine Learning

Conformal Prediction for Reliable Machine Learning
PDF, ePUB
  • eBook:
    Conformal Prediction for Reliable Machine Learning: Theory, Adaptations and Applications
  • Author:
    Vineeth Balasubramanian, Shen-Shyang Ho, Vladimir Vovk
  • Edition:
    1 edition
  • Categories:
  • Data:
    May 13, 2014
  • ISBN:
    0123985374
  • ISBN-13:
    9780123985378
  • Language:
    English
  • Pages:
    334 pages
  • Format:
    PDF, ePUB

Book 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.

Content

PART 1 - THEORY
CHAPTER 1. The Basic Conformal Prediction Framework
CHAPTER 2. Beyond the Basic Conformal Prediction Framework

PART 2 - ADAPTATIONS
CHAPTER 3. Active Learning
CHAPTER 4. Anomaly Detection
CHAPTER 5. Online Change Detection
CHAPTER 6. Feature Selection
CHAPTER 7. Model Selection
CHAPTER 8. Prediction Quality Assessment
CHAPTER 9. Other Adaptations

PART 3 - APPLICATIONS
CHAPTER 10. Biometrics and Robust Face Recognition
CHAPTER 11. Biomedical Applications: Diagnostic and Prognostic
CHAPTER 12. Network Traffic Classification and Demand Prediction
CHAPTER 13. Other Applications

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