Titlebook: Machine Learning Safety; Xiaowei Huang,Gaojie Jin,Wenjie Ruan Textbook 2023 The Editor(s) (if applicable) and The Author(s), under exclusi

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Safety and Security Propertiesely on the training and test datasets, without considering other factors that might potentially compromise the performance of a machine learning model. Actually, safety risks may appear at any stage of the lifecycle of a machine learning model. In recent years, the discussion on the potential risks

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Naive Bayesmany practical applications such as the spam filter email application. In the following, we will first introduce the learning algorithm, and then discuss how the naive Bayes may be attacked with respect to the safety properties we discussed in Chap. ..

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Loss Function and Gradient Descent is to optimise a certain loss function over a set of training instances. A carefully designed loss function can significantly improve the performance of the trained model. A recent trend in machine learning research—as we will explain later in Part III—also designs loss functions to integrate safet

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