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  2. Feature scaling - Wikipedia

    en.wikipedia.org/wiki/Feature_scaling

    [1] It's also important to apply feature scaling if regularization is used as part of the loss function (so that coefficients are penalized appropriately). Empirically, feature scaling can improve the convergence speed of stochastic gradient descent. In support vector machines, [2] it can reduce the time to find support vectors. Feature scaling ...

  3. Non-negative matrix factorization - Wikipedia

    en.wikipedia.org/wiki/Non-negative_matrix...

    Collective (joint) factorization: factorizing multiple interrelated matrices for multiple-view learning, e.g. multi-view clustering, see CoNMF [86] and MultiNMF [87] Cohen and Rothblum 1993 problem: whether a rational matrix always has an NMF of minimal inner dimension whose factors are also rational.

  4. pandas (software) - Wikipedia

    en.wikipedia.org/wiki/Pandas_(software)

    By default, a Pandas index is a series of integers ascending from 0, similar to the indices of Python arrays. However, indices can use any NumPy data type, including floating point, timestamps, or strings. [4]: 112 Pandas' syntax for mapping index values to relevant data is the same syntax Python uses to map dictionary keys to values.

  5. Softmax function - Wikipedia

    en.wikipedia.org/wiki/Softmax_function

    The softmax function, also known as softargmax [1]: 184 or normalized exponential function, [2]: 198 converts a vector of K real numbers into a probability distribution of K possible outcomes. It is a generalization of the logistic function to multiple dimensions, and is used in multinomial logistic regression .

  6. Lagrange multiplier - Wikipedia

    en.wikipedia.org/wiki/Lagrange_multiplier

    Suppose that we wish to find the stationary points of a smooth function : when restricted to the submanifold defined by = , where : is a smooth function for which 0 is a regular value. Let d ⁡ f {\displaystyle \ \operatorname {d} f\ } and d ⁡ g {\displaystyle \ \operatorname {d} g\ } be the exterior derivatives of f {\displaystyle \ f ...

  7. Multi-objective optimization - Wikipedia

    en.wikipedia.org/wiki/Multi-objective_optimization

    Multi-objective optimization or Pareto optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, or multiattribute optimization) is an area of multiple-criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously.

  8. Rijndael MixColumns - Wikipedia

    en.wikipedia.org/wiki/Rijndael_MixColumns

    void gmix_column (unsigned char * r) {unsigned char a [4]; unsigned char b [4]; unsigned char c; unsigned char h; /* The array 'a' is simply a copy of the input array 'r' * The array 'b' is each element of the array 'a' multiplied by 2 * in Rijndael's Galois field * a[n] ^ b[n] is element n multiplied by 3 in Rijndael's Galois field */ for (c = 0; c < 4; c ++) {a [c] = r [c]; /* h is set to ...

  9. Window function - Wikipedia

    en.wikipedia.org/wiki/Window_function

    A popular window function, the Hann window. Most popular window functions are similar bell-shaped curves. In signal processing and statistics, a window function (also known as an apodization function or tapering function [1]) is a mathematical function that is zero-valued outside of some chosen interval. Typically, window functions are ...