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  2. ggplot2 - Wikipedia

    en.wikipedia.org/wiki/Ggplot2

    ggplot2 is an open-source data visualization package for the statistical programming language R.Created by Hadley Wickham in 2005, ggplot2 is an implementation of Leland Wilkinson's Grammar of Graphics—a general scheme for data visualization which breaks up graphs into semantic components such as scales and layers. ggplot2 can serve as a replacement for the base graphics in R and contains a ...

  3. Large margin nearest neighbor - Wikipedia

    en.wikipedia.org/wiki/Large_Margin_Nearest_Neighbor

    Large margin nearest neighbor (LMNN) [1] classification is a statistical machine learning algorithm for metric learning. It learns a pseudometric designed for k-nearest neighbor classification. The algorithm is based on semidefinite programming , a sub-class of convex optimization .

  4. Type-2 fuzzy sets and systems - Wikipedia

    en.wikipedia.org/wiki/Type-2_fuzzy_sets_and_systems

    Figure 1. The membership function of a general type-2 fuzzy set is three-dimensional. A cross-section of one slice of the third dimension is shown. This cross-section, as well as all others, sits on the FOU. Only the boundary of the cross-section is used to describe the membership function of a general type-2 fuzzy set.

  5. Loss functions for classification - Wikipedia

    en.wikipedia.org/wiki/Loss_functions_for...

    These are called margin-based loss functions. Choosing a margin-based loss function amounts to choosing ϕ {\displaystyle \phi } . Selection of a loss function within this framework impacts the optimal f ϕ ∗ {\displaystyle f_{\phi }^{*}} which minimizes the expected risk, see empirical risk minimization .

  6. Hinge loss - Wikipedia

    en.wikipedia.org/wiki/Hinge_loss

    The plot shows that the Hinge loss penalizes predictions y < 1, corresponding to the notion of a margin in a support vector machine. In machine learning, the hinge loss is a loss function used for training classifiers. The hinge loss is used for "maximum-margin" classification, most notably for support vector machines (SVMs). [1]

  7. Margin (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Margin_(machine_learning)

    A margin classifier is a classification model that utilizes the margin of each example to learn such classification. There are theoretical justifications (based on the VC dimension ) as to why maximizing the margin (under some suitable constraints) may be beneficial for machine learning and statistical inference algorithms.

  8. Typographic alignment - Wikipedia

    en.wikipedia.org/wiki/Typographic_alignment

    justified—text is aligned along the left margin, with letter-spacing and word-spacing adjusted so that the text falls flush with both margins, also known as fully justified or full justification; centered—text is aligned to neither the left nor right margin; there is an even gap on each side of each line.

  9. Fréchet distribution - Wikipedia

    en.wikipedia.org/wiki/Fréchet_distribution

    One test to assess whether a multivariate distribution is asymptotically dependent or independent consists of transforming the data into standard Fréchet margins using the transformation = / ⁡ and then mapping from Cartesian to pseudo-polar coordinates (,) = (+, / (+)).