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.

Key Features

  • Understand the theoretical foundations of this important framework that can provide a reliable measure of confidence with predictions in machine learning
  • Be able to apply this framework to real-world problems in different machine learning settings, including classification, regression, and clustering
  • Learn effective ways of adapting the framework to newer problem settings, such as active learning, model selection, or change detection

Readership

Primary Audience: Professors, Research Professors, Assistants/Associates, Professional Research/R&D Engineers, Research Scientists, Graduate Students

Secondary Audience: clinical researchers, researchers in biomedical informatics, national security, financial analysts, undergraduate students of applied machine learning, pattern recognition, artificial intelligence, clinical informatics

Table of Contents

  • Contributing Authors
  • Foreword
  • Preface
    • Book Organization
    • Part I: Theory
    • Part II: Adaptations
    • Part III: Applications
    • Companion Website
    • Contacting Us
    • Acknowledgments
  • Part 1: Theory
    • Chapter 1. The Basic Conformal Prediction Framework
      • Abstract
      • Acknowledgments
      • 1.1 The Basic Setting and Assumptions
      • 1.2 Set and Confidence Predictors
      • 1.3 Conformal Prediction
      • 1.4 Efficiency in the Case of Prediction without Objects
      • 1.5 Universality of Conformal Predictors
      • 1.6 Structured Case and Classification
      • 1.7 Regression
      • 1.8 Additional Properties of Validity and Efficiency in the Online Framework
    • Chapter 2. Beyond the Basic Conformal Prediction Framework
      • Abstract
      • Acknowledgments
      • 2.1 Conditional Validity
      • 2.2 Conditional Conformal Predictors
      • 2.3 Inductive Conformal Predictors
      • 2.4 Training Conditional Validity of Inductive Conformal Predictors
      • 2.5 Classical Tolerance Regions
      • 2.6 Object Conditional Validity and Efficiency
      • 2.7 Label Conditional Validity and ROC Curves
      • 2.8 Venn Predictors
  • Part 2: Adaptations
    • Chapter 3. Active Learning
      • Abstract
      • Acknowledgments
      • 3.1 Introduction
      • 3.2 Background and Related Work
      • 3.3 Active Learning Using Conformal Prediction
      • 3.4 Experimental Results
      • 3.5 Discussion and Conclusions
    • Chapter 4. Anomaly Detection
      • Abstract
      • 4.1 Introduction
      • 4.2 Background
      • 4.3 Conformal Prediction for Multiclass Anomaly Detection
      • 4.4 Conformal Anomaly Detection
      • 4.5 Inductive Conformal Anomaly Detection
      • 4.6 Nonconformity Measures for Exam

Details

No. of pages:
334
Language:
English
Copyright:
© 2014
Published:
Imprint:
Morgan Kaufmann
Print ISBN:
9780123985378
Electronic ISBN:
9780124017153

Reviews

"...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."--Zentralblatt MATH, Sep-14

"...the book is highly recommended for people looking for formal machine learning techniques that can guarantee theoretical soundness and reliability."--Computing Reviews,December 4,2014

"This book captures the basic theory of the framework, demonstrates how the framework can be applied to real-world problems, and also presents several adaptations of the framework…" - HPCMagazine.com, August 2014