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  2. Support vector machine - Wikipedia

    en.wikipedia.org/wiki/Support_vector_machine

    Structured support-vector machine is an extension of the traditional SVM model. While the SVM model is primarily designed for binary classification, multiclass classification, and regression tasks, structured SVM broadens its application to handle general structured output labels, for example parse trees, classification with taxonomies ...

  3. 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]

  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. Radial basis function kernel - Wikipedia

    en.wikipedia.org/wiki/Radial_basis_function_kernel

    In machine learning, the radial basis function kernel, or RBF kernel, is a popular kernel function used in various kernelized learning algorithms. In particular, it is commonly used in support vector machine classification. [1]

  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. Regularization perspectives on support vector machines

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

    Within mathematical analysis, Regularization perspectives on support-vector machines provide a way of interpreting support-vector machines (SVMs) in the context of other regularization-based machine-learning algorithms. SVM algorithms categorize binary data, with the goal of fitting the training set data in a way that minimizes the average of ...

  8. Polynomial kernel - Wikipedia

    en.wikipedia.org/wiki/Polynomial_kernel

    The hyperplane learned in feature space by an SVM is an ellipse in the input space. In machine learning , the polynomial kernel is a kernel function commonly used with support vector machines (SVMs) and other kernelized models, that represents the similarity of vectors (training samples) in a feature space over polynomials of the original ...

  9. LIBSVM - Wikipedia

    en.wikipedia.org/wiki/LIBSVM

    LIBSVM and LIBLINEAR are two popular open source machine learning libraries, both developed at the National Taiwan University and both written in C++ though with a C API. LIBSVM implements the sequential minimal optimization (SMO) algorithm for kernelized support vector machines (SVMs), supporting classification and regression. [1]