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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. 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]

  4. Least-squares support vector machine - Wikipedia

    en.wikipedia.org/wiki/Least-squares_support...

    Least-squares support-vector machines (LS-SVM) for statistics and in statistical modeling, are least-squares versions of support-vector machines (SVM), which are a set of related supervised learning methods that analyze data and recognize patterns, and which are used for classification and regression analysis.

  5. Kernel method - Wikipedia

    en.wikipedia.org/wiki/Kernel_method

    In machine learning, kernel machines are a class of algorithms for pattern analysis, whose best known member is the support-vector machine (SVM). These methods involve using linear classifiers to solve nonlinear problems. [ 1 ]

  6. 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 .

  7. Radial basis function kernel - Wikipedia

    en.wikipedia.org/wiki/Radial_basis_function_kernel

    Because support vector machines and other models employing the kernel trick do not scale well to large numbers of training samples or large numbers of features in the input space, several approximations to the RBF kernel (and similar kernels) have been introduced. [4]

  8. Daughter Admits She Used to Throw Plates Away Rather Than ...

    www.aol.com/lifestyle/daughter-admits-she-used...

    The young woman, named Sam, shared a video on TikTok on Tuesday, Dec. 10, in which she and her parents took part in the viral “We Listen and We Don’t Judge” trend, which sees a group gather ...

  9. Category:Support vector machines - Wikipedia

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

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