One-Class SVM is a type of machine learning algorithm used for anomaly detection, which focuses on identifying outliers in a dataset by learning a decision boundary around the 'normal' class. This method is particularly useful when only one class of data is available for training, making it effective for applications like trend detection and influencer identification where the goal is to find patterns or unusual behaviors in data that predominantly belong to one category.
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