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In coding theory, the weight enumerator polynomial of a binary linear code specifies the number of words of each possible Hamming weight. Let C ⊂ F 2 n {\displaystyle C\subset \mathbb {F} _{2}^{n}} be a binary linear code of length n {\displaystyle n} .
Common to all versions are a set of n items, with each item having an associated profit p j and weight w j. The binary decision variable x j is used to select the item. The objective is to pick some of the items, with maximal total profit, while obeying that the maximum total weight of the chosen items must not exceed W .
The definition of an A p weight and the reverse Hölder inequality indicate that such a weight cannot degenerate or grow too quickly. This property can be phrased equivalently in terms of how much the logarithm of the weight oscillates: (a) If w ∈ A p, (p ≥ 1), then log(w) ∈ BMO (i.e. log(w) has bounded mean oscillation).
A special case of constant weight codes are the one-of-N codes, that encode bits in a code-word of bits. The one-of-two code uses the code words 01 and 10 to encode the bits '0' and '1'. A one-of-four code can use the words 0001, 0010, 0100, 1000 in order to encode two bits 00, 01, 10, and 11.
In error-correcting coding, the minimum Hamming weight, commonly referred to as the minimum weight w min of a code is the weight of the lowest-weight non-zero code word. The weight w of a code word is the number of 1s in the word. For example, the word 11001010 has a weight of 4. In a linear block code the minimum weight is also the minimum ...
In this case, player allocates higher weight to the actions that had a better outcome and choose his strategy relying on these weights. In machine learning , Littlestone applied the earliest form of the multiplicative weights update rule in his famous winnow algorithm , which is similar to Minsky and Papert's earlier perceptron learning algorithm .
In order to apply the disparity filter algorithm without overlooking nodes with low strength, a normalized weight p ij is defined as p ij = w ij /s i. In the null model, the normalized weights of a certain node with degree k is generated like this: k − 1 pins are randomly assigned between the interval 0 and 1.
In computational geometry, the point-in-polygon (PIP) problem asks whether a given point in the plane lies inside, outside, or on the boundary of a polygon. It is a special case of point location problems and finds applications in areas that deal with processing geometrical data, such as computer graphics , computer vision , geographic ...