Conformal Prediction for Reliable Machine Learning (eBook, ePUB)

Theory, Adaptations and Applications
Redaktion: Balasubramanian, Vineeth; Vovk, Vladimir; Ho, Shen-Shyang

• Format: ePub

Produktbeschreibung

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

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Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 334
• Erscheinungstermin: 23. April 2014
• Englisch
• ISBN-13: 9780124017153
• Artikelnr.: 41044666

Autorenporträt

Vineeth N Balasubramanian is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology, Hyderabad, India. Until July 2013, he was an Assistant Research Professor at the Center for Cognitive Ubiquitous Computing (CUbiC) at Arizona State University (ASU). He holds dual Masters degrees in Mathematics (2001) and Computer Science (2003) from Sri Sathya Sai University, India, and worked at Oracle Corporation for two years until 2005. His PhD dissertation (2010) on the Conformal Predictions framework was nominated for the Outstanding PhD Dissertation at the Department of Computer Science at ASU, as well as for the annual ACM Doctoral Dissertation Award. He was also a recipient of the Gold Medals for Academic Excellence for his performances in the Bachelors program in Math in 1999, and for his Masters program in Computer Science in 2003. His research interests include pattern recognition, machine learning, computer vision and multim

edia computing within assistive and healthcare applications. His current research includes extending the Conformal Predictions framework to real-world problem contexts, and newer machine learning problems such as active learning and transfer learning.

Inhaltsangabe

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

Rezensionen

"...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