How are the k-nearest neigh-bors (knn) algorithms different from k-means clustering?
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1. Consider the following algorithm: for ( i = 1 ; i <= 1 . 5 n ; i++) cout << i ; for ( i = n ; i >= 1 ; i - - ) cout << i ; (a) What is the output when n = 2, n = 4, and n = 6? (b) What is the time complexity T(n)? You may assume that the input n is divisible by 2.
Can you list some use cases where classification machine learning algorithms can be used?
Give an algorithm for the following problem. Given a list of n distinct positive integers, partition the list into two sublists, each of size n/2, such that the difference between the sums of the integers in the two sublists is minimized. You may assume that n is a multiple of 2.
Which is the most straight forward approach for planning algorithm?
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Consider the following algorithm: for ( i = 2 ; i <= n ; i++) { for ( j = 0 ; j <= n) { cout << i << j ; j = j + floor(n/4) ; } } (a) What is the output when n = 4 (b) What is the time complexity T(n). You may assume that n is divisible 4.
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