= Solution
The fitted LDA rule replaces $\pi_l,\mu_l,\Sigma$ by class proportions, class sample means, and the pooled within-class covariance, then maximizes the resulting $\widehat\delta_l(x)$. Means and covariances have unbounded sensitivity, so a gross outlier can substantially move every LDA boundary. A soft-margin linear <support vector machine> uses hinge loss; observations beyond the correctly classified margin cease contributing, though mislabeled or extreme points can still matter according to the penalty $C$. Thus the SVM is generally more robust to well-classified extremes, while neither method is automatically robust to adversarial outliers.
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