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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. Regularization perspectives on support vector machines

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

    The training and test-set errors can be measured without bias and in a fair way using accuracy, precision, Auc-Roc, precision-recall, and other metrics. Regularization perspectives on support-vector machines interpret SVM as a special case of Tikhonov regularization, specifically Tikhonov regularization with the hinge loss for a loss function.

  4. 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 .

  5. Kernel method - Wikipedia

    en.wikipedia.org/wiki/Kernel_method

    Kernel methods owe their name to the use of kernel functions, which enable them to operate in a high-dimensional, implicit feature space without ever computing the coordinates of the data in that space, but rather by simply computing the inner products between the images of all pairs of data in the feature space. This operation is often ...

  6. 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.

  7. Training, validation, and test data sets - Wikipedia

    en.wikipedia.org/wiki/Training,_validation,_and...

    Finally, the test data set is a data set used to provide an unbiased evaluation of a final model fit on the training data set. [5] If the data in the test data set has never been used in training (for example in cross-validation), the test data set is also called a holdout data set. The term "validation set" is sometimes used instead of "test ...

  8. Psychological testing - Wikipedia

    en.wikipedia.org/wiki/Psychological_testing

    Psychological tests can include a series of tasks, problems to solve, and characteristics (e.g., behaviors, symptoms) the presence of which the respondent affirms/denies to varying degrees. Psychological tests can include questionnaires and interviews. Questionnaire- and interview-based scales typically differ from psychoeducational tests ...

  9. Stroboscopic effect - Wikipedia

    en.wikipedia.org/wiki/Stroboscopic_effect

    SVM can be used for objective assessment by a human observer of visible stroboscopic effects of temporal light modulation of lighting equipment in general indoor applications, with typical indoor light levels (> 100 lx) and with moderate movements of an observer or a nearby handled object (< 4 m/s). For assessing unwanted stroboscopic effects ...