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SVMs can be used to solve various real-world problems: SVMs are helpful in text and hypertext categorization, as their application can significantly reduce the need for labeled training instances in both the standard inductive and transductive settings. [9] Some methods for shallow semantic parsing are based on support vector machines. [10]
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.
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]
In this version one finds the solution by solving a set of linear equations instead of a convex quadratic programming (QP) problem for classical SVMs. Least-squares SVM classifiers were proposed by Johan Suykens and Joos Vandewalle. [1] LS-SVMs are a class of kernel-based learning methods.
This traditional geometric interpretation of SVMs provides useful intuition about how SVMs work, but is difficult to relate to other machine-learning techniques for avoiding overfitting, like regularization, early stopping, sparsity and Bayesian inference.
In machine learning, a ranking SVM is a variant of the support vector machine algorithm, which is used to solve certain ranking problems (via learning to rank).The ranking SVM algorithm was published by Thorsten Joachims in 2002. [1]
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The hinge loss is used for "maximum-margin" classification, most notably for support vector machines (SVMs). [1] For an intended output t = ±1 and a classifier score y, the hinge loss of the prediction y is defined as = (,)