Neyman-Pearson Lemma and Receiver Operating Characteristic Curve
A slecture by ECE student Soonam Lee
Partly based on the ECE662 Spring 2014 lecture material of Prof. Mireille Boutin.
Introduction
The purpose of this slecture is understanding Neyman-Pearson Lemma and Receiver Operating Characteristic (ROC) curve from theory to application. The fundamental theories stem from statistics and these can be used for signal detection and classification. In order to firmly understand these concepts, we will review statistical concept first including ``\emph{False alarm} and ``\emph{Miss}. After that, we will consider Bayes' decision rule using cost function. Next, we visit Neyman-Pearson test and minimax test. Lastly, we will discuss ROC curves. Note that we only consider two classes case in this slecture, but the concept can be extended to multiple classes.
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