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    Conformal Prediction for Reliable Machine Learning

    By: Unknown

    $0.00
     
     
    ISBN
    9780124017153
    Date Released
    Binding
    eBook
     
     

    Out of Print

    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.

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

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