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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.
Scanning voltage microscopy; Secure Virtual Machine, a virtualization technology by AMD; Shared Virtual Memory, another AMD technology for computation on its GPUs with HSA/ROCm.
Last release from 2016, forked as Breezy: Distributed and Client–server: Merge GPL-2.0-or-later: Unix-like, Windows, macOS: Free BitKeeper: BitMover Inc. Unmaintained; last updated December 29, 2018 Distributed: Merge Apache-2.0: Unix-like, Windows, macOS: Free IBM DevOps Code ClearCase: IBM Rational: Active Client–server: Merge or lock [nb ...
The Unscrambler – free-to-try commercial multivariate analysis software for Windows; Unistat – general statistics package that can also work as Excel add-in; WarpPLS – statistics package used in structural equation modeling; Wolfram Language [8] – the computer language that evolved from the program Mathematica. It has similar ...
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 ...
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 .
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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.