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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.
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 contrast, data mining uses machine learning and statistical models to uncover clandestine or hidden patterns in a large volume of data. [ 8 ] The related terms data dredging , data fishing , and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are (or may be) too small for reliable ...
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.
ODM offers a choice of well-known machine learning approaches such as Decision Trees, Naive Bayes, Support vector machines, Generalized linear model (GLM) for predictive mining, Association rules, K-means and Orthogonal Partitioning [1] [2] Clustering, and Non-negative matrix factorization for descriptive mining.
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. This provides a theoretical framework with which to analyze SVM algorithms and compare them to other algorithms with the same goals: to generalize ...
One other popular approach is the Recursive Feature Elimination algorithm, [15] commonly used with Support Vector Machines to repeatedly construct a model and remove features with low weights. Embedded methods are a catch-all group of techniques which perform feature selection as part of the model construction process.
In machine learning, Platt scaling or Platt calibration is a way of transforming the outputs of a classification model into a probability distribution over classes.The method was invented by John Platt in the context of support vector machines, [1] replacing an earlier method by Vapnik, but can be applied to other classification models. [2]