Search results
Results From The WOW.Com Content Network
Minimax (sometimes Minmax, MM[ 1] or saddle point[ 2]) is a decision rule used in artificial intelligence, decision theory, game theory, statistics, and philosophy for minimizing the possible loss for a worst case ( max imum loss) scenario. When dealing with gains, it is referred to as "maximin" – to maximize the minimum gain.
Also known as min-max scaling or min-max normalization, rescaling is the simplest method and consists in rescaling the range of features to scale the range in [0, 1] or [−1, 1]. Selecting the target range depends on the nature of the data. The general formula for a min-max of [0, 1] is given as: [3]
Arg max. As an example, both unnormalised and normalised sinc functions above have of {0} because both attain their global maximum value of 1 at x = 0. The unnormalised sinc function (red) has arg min of {−4.49, 4.49}, approximately, because it has 2 global minimum values of approximately −0.217 at x = ±4.49.
A min-max heap is a complete binary tree containing alternating min (or even) and max (or odd) levels. Even levels are for example 0, 2, 4, etc, and odd levels are respectively 1, 3, 5, etc. We assume in the next points that the root element is at the first level, i.e., 0. Example of Min-max heap.
The procedure for deleting the root from the heap (effectively extracting the maximum element in a max-heap or the minimum element in a min-heap) while retaining the heap property is as follows: Replace the root of the heap with the last element on the last level. Compare the new root with its children; if they are in the correct order, stop.
Ford–Fulkerson algorithm. The Ford–Fulkerson method or Ford–Fulkerson algorithm ( FFA) is a greedy algorithm that computes the maximum flow in a flow network. It is sometimes called a "method" instead of an "algorithm" as the approach to finding augmenting paths in a residual graph is not fully specified [ 1] or it is specified in several ...
In mathematical optimization, the proximal operator is an operator associated with a proper, [note 1] lower semi-continuous convex function from a Hilbert space to [, +], and is defined by: [1]
Minimax theorem. In the mathematical area of game theory, a minimax theorem is a theorem providing conditions that guarantee that the max–min inequality is also an equality. The first theorem in this sense is von Neumann 's minimax theorem about zero-sum games published in 1928, [ 1] which was considered the starting point of game theory.