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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 .
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.
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.
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.
A video file format is a type of file format for storing digital video data on a computer system. Video is almost always stored using lossy compression to reduce the file size. A video file normally consists of a container (e.g. in the Matroska format) containing visual (video without audio) data in a video coding format (e.g. VP9 ) alongside ...
Interactive Forms is a mechanism to add forms to the PDF file format. PDF currently supports two different methods for integrating data and PDF forms. Both formats today coexist in the PDF specification: [37] [52] [53] [54] AcroForms (also known as Acrobat forms), introduced in the PDF 1.2 format specification and included in all later PDF ...
Experts say vehicle-based attacks are simple for a 'lone wolf' terrorist to plan and execute, and challenging for authorities to prevent.
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 ...