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  2. Support vector machine - Wikipedia

    en.wikipedia.org/wiki/Support_vector_machine

    The soft-margin support vector machine described above is an example of an empirical risk minimization (ERM) algorithm for the hinge loss. Seen this way, support vector machines belong to a natural class of algorithms for statistical inference, and many of its unique features are due to the behavior of the hinge loss.

  3. Vladimir Vapnik - Wikipedia

    en.wikipedia.org/wiki/Vladimir_Vapnik

    Vladimir Naumovich Vapnik (Russian: Владимир Наумович Вапник; born 6 December 1936) is a computer scientist, researcher, and academic.He is one of the main developers of the Vapnik–Chervonenkis theory of statistical learning [1] and the co-inventor of the support-vector machine method and support-vector clustering algorithms.

  4. Space vector modulation - Wikipedia

    en.wikipedia.org/wiki/Space_vector_modulation

    Space vector modulation (SVM) is an algorithm for the control of pulse-width modulation (PWM), invented by Gerhard Pfaff, Alois Weschta, and Albert Wick in 1982. [ 1 ] [ 2 ] It is used for the creation of alternating current (AC) waveforms ; most commonly to drive 3 phase AC powered motors at varying speeds from DC using multiple class-D ...

  5. Category:Support vector machines - Wikipedia

    en.wikipedia.org/wiki/Category:Support_vector...

    Support vector machine; H. Hinge loss; L. Least-squares support vector machine; M. Margin (machine learning) R. Radial basis function kernel; Ranking SVM;

  6. SVM - Wikipedia

    en.wikipedia.org/wiki/SVM

    Support vector machine, a machine learning algorithm; Stroboscopic effect visibility measure (SVM), a measure for assessing a type of temporal light artefacts; Other

  7. Structured support vector machine - Wikipedia

    en.wikipedia.org/wiki/Structured_support_vector...

    The structured support-vector machine is a machine learning algorithm that generalizes the Support-Vector Machine (SVM) classifier. Whereas the SVM classifier supports binary classification , multiclass classification and regression , the structured SVM allows training of a classifier for general structured output labels .

  8. Regularization perspectives on support vector machines

    en.wikipedia.org/wiki/Regularization...

    SVM algorithms categorize binary data, with the goal of fitting the training set data in a way that minimizes the average of the hinge-loss function and L2 norm of the learned weights. This strategy avoids overfitting via Tikhonov regularization and in the L2 norm sense and also corresponds to minimizing the bias and variance of our estimator ...

  9. Russian Wikipedia - Wikipedia

    en.wikipedia.org/wiki/Russian_Wikipedia

    It is the largest Wikipedia written in any Slavic language, surpassing its nearest rival, the Polish Wikipedia, by 20% in terms of the number of articles and fivefold by the parameter of depth. [4] In addition, the Russian Wikipedia is the largest Wikipedia written in Cyrillic [5] or in a script other than the Latin script. In April 2016, the ...