SVMs directly give us the posterior probabilities P(y = 1jx) and P(y = 􀀀1jx)

Related MCQs

• SVMs directly give us the posterior probabilities P(y = 1jx) and P(y = ??1jx)
• Let S1 and S2 be the set of support vectors and w1 and w2 be the learnt weight vectors for a linearly separable problem using hard and soft margin linear SVMs respectively. Which of the following are correct?
• The SVMs are less effective when
• Linear SVMs have no hyperparameters that need to be set by cross-validation
• Linear SVMs have no hyperparameters
• Linear SVMs have no hyperparameters that need to be set by cross-validation
• Linear SVMs have no hyperparameters that need to be set by cross-valid
• Suppose there are 25 base classifiers. Each classifier has error rates of e = 0.35. Suppose you are using averaging as ensemble technique. What will be the probabilities that ensemble of above 25 classifiers will make a wrong prediction? Note: All classifiers are independent of each other
• Which of the following quantities are minimized directly or indirectly during parameter estimation in Gaussian distribution Model?
• which of the following cases will K-Means clustering give poor results? 1. Data points with outliers 2. Data points with different densities 3. Data points with round shapes 4. Data points with non-convex shapes