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  2. 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 ...

  3. 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 ...

  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. Kernel perceptron - Wikipedia

    en.wikipedia.org/wiki/Kernel_perceptron

    Here, K is some kernel function. Formally, a kernel function is a non-negative semidefinite kernel (see Mercer's condition), representing an inner product between samples in a high-dimensional space, as if the samples had been expanded to include additional features by a function Φ: K(x, x') = Φ(x) · Φ(x').

  7. Structured support vector machine - Wikipedia

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

    The design of this function depends very much on the application. Because the regularized risk function above is non-differentiable, it is often reformulated in terms of a quadratic program by introducing one slack variable for each sample, each representing the value of the maximum. The standard structured SVM primal formulation is given as ...

  8. List of Microsoft Office filename extensions - Wikipedia

    en.wikipedia.org/wiki/List_of_Microsoft_Office...

    Excel-related file extensions of this format include:.xlsx – Excel workbook.xlsm – Excel macro-enabled workbook; same as xlsx but may contain macros and scripts.xltx – Excel template.xltm – Excel macro-enabled template; same as xltx but may contain macros and scripts; Other formats Microsoft Excel uses dedicated file formats that are ...

  9. Gauss–Newton algorithm - Wikipedia

    en.wikipedia.org/wiki/Gauss–Newton_algorithm

    It is an extension of Newton's method for finding a minimum of a non-linear function. Since a sum of squares must be nonnegative, the algorithm can be viewed as using Newton's method to iteratively approximate zeroes of the components of the sum, and thus minimizing the sum.