Suppose you run one iteration of the Adaboost algorithm for a two-class classification problem that combines a set of classifiers. Initially, all data points Xn have equal weights Wn. After training the first classifier, you find that its accuracy is 55%. Indicate whether the following statements are true or false, and explain why: (a) The weight a1 assigned to the first classifier will be negative. (b) For correctly classified points, Wn(2) will be smaller than Wn(1). (c) The weights Wn(2) for misclassified points will be increased by an amount that depends on their distance from the decision boundary.