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  2. Heejin - Wikipedia

    en.wikipedia.org/wiki/Heejin

    Jeon Hee-jin (Korean: 전희진, Korean pronunciation: [tɕʌn çidʑin]; born October 19, 2000), known mononymously as Heejin (occasionally stylized as HeeJin) is a South Korean singer. She is a member of Loona , its sub-unit Loona 1/3 , and Artms .

  3. Mathematics of artificial neural networks - Wikipedia

    en.wikipedia.org/wiki/Mathematics_of_artificial...

    Backpropagation training algorithms fall into three categories: steepest descent (with variable learning rate and momentum, resilient backpropagation); quasi-Newton (Broyden–Fletcher–Goldfarb–Shanno, one step secant);

  4. Comparison gallery of image scaling algorithms - Wikipedia

    en.wikipedia.org/wiki/Comparison_gallery_of...

    Then the resolution-independent version is rendered as a raster image at the desired resolution. This technique is used by Adobe Illustrator Live Trace, Inkscape, and several recent papers. [6] Scalable Vector Graphics are well suited to simple geometric images, while photographs do not fare well with vectorization due to their complexity.

  5. Greedy coloring - Wikipedia

    en.wikipedia.org/wiki/Greedy_coloring

    The right example generalises to 2-colorable graphs with n vertices, where the greedy algorithm expends n/2 colors. In the study of graph coloring problems in mathematics and computer science , a greedy coloring or sequential coloring [ 1 ] is a coloring of the vertices of a graph formed by a greedy algorithm that considers the vertices of the ...

  6. Algorithmic Puzzles - Wikipedia

    en.wikipedia.org/wiki/Algorithmic_Puzzles

    The puzzles in the book cover a wide range of difficulty, and in general do not require more than a high school level of mathematical background. [3] William Gasarch notes that grouping the puzzles only by their difficulty and not by their themes is actually an advantage, as it provides readers with fewer clues about their solutions.

  7. Neuroevolution of augmenting topologies - Wikipedia

    en.wikipedia.org/wiki/Neuroevolution_of...

    NeuroEvolution of Augmenting Topologies (NEAT) is a genetic algorithm (GA) for the generation of evolving artificial neural networks (a neuroevolution technique) developed by Kenneth Stanley and Risto Miikkulainen in 2002 while at The University of Texas at Austin. It alters both the weighting parameters and structures of networks, attempting ...

  8. Haar-like feature - Wikipedia

    en.wikipedia.org/wiki/Haar-like_feature

    Integral images can be defined as two-dimensional lookup tables in the form of a matrix with the same size of the original image. Each element of the integral image contains the sum of all pixels located on the up-left region of the original image (in relation to the element's position).

  9. Sieve of Atkin - Wikipedia

    en.wikipedia.org/wiki/Sieve_of_Atkin

    The following is pseudocode which combines Atkin's algorithms 3.1, 3.2, and 3.3 [1] by using a combined set s of all the numbers modulo 60 excluding those which are multiples of the prime numbers 2, 3, and 5, as per the algorithms, for a straightforward version of the algorithm that supports optional bit-packing of the wheel; although not specifically mentioned in the referenced paper, this ...