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4208Larger Gamma in an RBF KernelA soft-margin SVM keeps ||w|| 2 and total hinge loss fixed, but C increases from 0.5 to 1.5 while total hinge loss is 2.0. How much does the objective increase?机器学习中等derivation未尝试面试订阅4209Smaller Gamma in an RBF KernelAt a test point, one support vector contributes +0.9. If its dual coefficient is halved and everything else stays fixed, what contribution remains from that support vector?机器学习中等derivation未尝试面试订阅4210When Kernels Beat Manual Feature LiftsA linear SVM score at a point is 1.2 before feature rescaling. If the relevant feature values are all doubled while w is held fixed, what new score contribution comes from that linear term?机器学习中等derivation未尝试面试订阅4211Why Only Support Vectors Matter at Prediction TimeIf your features are extremely sparse one-hot indicators with huge dimension, would you usually try a linear SVM or an RBF SVM first, and why?机器学习中等essay未尝试面试订阅4212Why the Dual Is Attractive in Kernel SVMsWhy is feature scaling especially important before using an RBF SVM?机器学习中等essay未尝试面试订阅4213Why Hard Margin Is FragileWhy do only support vectors matter at prediction time in an SVM?机器学习中等essay未尝试面试订阅4214Why Kernels Are Not FreeWhy can choosing C extremely large increase overfitting risk in a soft-margin SVM?机器学习中等essay未尝试面试订阅4215A Fast Sanity Check for Kernel-SVM AnswersWhy can a high-degree polynomial kernel become numerically and statistically awkward on unscaled features?机器学习中等essay未尝试面试订阅