Suppose we train a hard-margin linear svm on n > 100 datapoints in r₂, yielding a hyperplane with exactly 2 support vectors. if we add one more datapoint 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)? select one of: {2, 3, n, n + 1}. optional: draw a case that justifies your answer

Answer soon(fast) 15 pts-brainliest? for right answer with small explination a restaurant catered a party for 40 people. a child’s dinner (c) cost $11 and an adult’s dinner (a) cost $20. the total cost of the dinner was $728. how many children and adults were at the party? use the table to guess and check.(i couldn't get the graph onto the question)                                8 children and 32 adults9 children and 31 adults10 children and 30 adults12 children and 28 adults

Which of the following transformations will always produce a congruent figure?

From the beginning of the day the temperature rose 15 degrees so that by 4 pm it was 87 degrees. what was the temperature at the start of the day