In regression the goal is to minimize the least squares cost function shown belo

Question

In regression the goal is to minimize the least squares cost function shown below. Sigma^P_p=1 (b + x^T_P W - y_p)^2 Through this minimization The best set of outputs green the inputs from the training set, is recovered. The best pair (b, w) is recovered. The best set of inputs given the expected outputs from the training set, is recovered. None of the above. When two classes of data are linearly separable, and infinite number of hyperplanes (decision boundaries) can be learned from that data. The "best" hyperplane is chosen as the one that Minimizes classification error on the training data Minimizes classification error on the test data Has the least value for its gradient Leaves the maximum margin from training data belonging to the two classes Breadth-first search Expands the shallowest nodes first Expands the node with lowest path cost Expands the deepest unexpanded node first None of the above Uniform-cost search Expands the shallowest nodes first Expands the node with lowest path cost Expands the deepest unexpanded node first None of the above Depth-first search Expands the shallowest nodes first Expands the node with lowest path cost Expands the deepest unexpanded node first None of the above Iterative deepening search calls uniform-cost search with increasing depth limits until a goal is found. (True/False) A heuristic function h(n) estimates the cost already paid to reach the node n estimates the cost of a solution as a function of number of iterations n estimates the coat of a solution from node n estimates the cost of a solution from the start node to the goal node n

Explanation / Answer

breadth first algorithm has option (a).

uniform cost seardh has answer of option (b)

depth first search has answer of option (c)

18. true

19. (c)

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