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?

Related MCQs

• Suppose we train a hard-margin linear SVM on n > 100 data points in R2, yielding a hyperplane with exactly 2 support vectors. If we add one more data point and retrain the classifier, what is the maximum possible number of support vectors for the new hyperplane (assuming the n + 1 points are linearly separable)?
• In a real problem, you should check to see if the SVM is separable and then include slack variables if it is not separable.
• The soft margin SVM is more preferred than the hard-margin SVM when-
• Suppose you are using a Linear SVM classifier with 2 class classification problem. Now you have been given the following data in which some points are circled red that are representing support vectors.If you remove the following any one red points from the data. Does the decision boundary will change?
• Suppose you are using a Linear SVM classifier with 2 class classification problem. Now you have been given the following data in which some points are circled red that are representing support vectors.If you remove the following any one red points from the data. Does the decision boundary will change?
• Which of the following methods can not achieve zero training error on any linearly separable dataset?
•               performs a PCA with non-linearly separable data sets.
• ________performs a PCA with non-linearly separable data sets.
• We have been given a dataset with n records in which we have input attribute as x and output attribute as y. Suppose we use a linear regression method to model this data. To test our linear regressor, we split the data in training set and test set randomly. Now we increase the training set size gradually. As the training set size increases, What do you expect will happen with the mean training error?
• We have been given a dataset with n records in which we have input attribute as x and output attribute as y. Suppose we use a linear regression method to model this data. To test our linear regressor, we split the data in training set and test set randomly. Now we increase the training set size gradually. As the training set size increases, what do you expect will happen with the mean training error?