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

✍ Scribed by Vineeth Balasubramanian, Shen-Shyang Ho and Vladimir Vovk (Eds.)

Publisher: Morgan Kaufmann
Year: 2014
Tongue: English
Leaves: 299
Edition: 1
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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.

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

✦ Table of Contents

Content:
Conformal Prediction for Reliable Machine Learning, Page i
Conformal Prediction for Reliable Machine Learning, Page iii
Copyright, Page iv
Copyright Permissions, Page v
Contributing Authors, Pages xiii-xiv
Foreword, Pages xv-xvi
Preface, Pages xvii-xxiii
Chapter 1 - The Basic Conformal Prediction Framework, Pages 3-19, Vladimir Vovk
Chapter 2 - Beyond the Basic Conformal Prediction Framework, Pages 21-46, Vladimir Vovk
Chapter 3 - Active Learning, Pages 49-70, Vineeth N. Balasubramanian, Shayok Chakraborty, Shen-Shyang Ho, Harry Wechsler, Sethuraman Panchanathan
Chapter 4 - Anomaly Detection, Pages 71-97, Rikard Laxhammar
Chapter 5 - Online Change Detection, Pages 99-114, Shen-Shyang Ho, Harry Wechsler
Chapter 6 - Feature Selection, Pages 115-130, Tony Bellotti, Ilia Nouretdinov, Meng Yang, Alexander Gammerman
Chapter 7 - Model Selection, Pages 131-143, David R. Hardoon, Zakria Hussain, John Shawe-Taylor
Chapter 8 - Prediction Quality Assessment, Pages 145-166, Matjaž Kukar
Chapter 9 - Other Adaptations, Pages 167-185, Vineeth N. Balasubramanian, Prasanth Lade, Hemanth Venkateswara, Evgueni Smirnov, Sethuraman Panchanathan
Chapter 10 - Biometrics and Robust Face Recognition, Pages 189-215, Harry Wechsler, Fayin Li
Chapter 11 - Biomedical Applications: Diagnostic and Prognostic, Pages 217-230, Ilia Nouretdinov, Tony Bellotti, Alexander Gammerman
Chapter 12 - Network Traffic Classification and Demand Prediction, Pages 231-259, Mikhail Dashevskiy, Zhiyuan Luo
Chapter 13 - Other Applications, Pages 261-271, Shen-Shyang Ho
Bibliography, Pages 273-293
Index, Pages 295-298

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