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• Support_vector_machine abstract "In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. Given a set of training examples, each marked for belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples into one category or the other, making it a non-probabilistic binary linear classifier. An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall on.In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces.When data are not labeled, a supervised learning is not possible, and an unsupervised learning is required, that would find natural clustering of the data to groups, and map new data to these formed groups. The clustering algorithm which provides an improvement to the support vector machines is called support vector clustering and is often used in industrial applications either when data is not labeled or when only some data is labeled as a preprocessing for a classification pass.".
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• Support_vector_machine wikiPageExternalLink svm.pdf.
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• Support_vector_machine wikiPageWikiLink Alexey_Chervonenkis.
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• Support_vector_machine wikiPageWikiLink Quadratic_programming.
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• Support_vector_machine wikiPageWikiLink Regression_analysis.
• Support_vector_machine wikiPageWikiLink Regularization_perspectives_on_support_vector_machines.
• Support_vector_machine wikiPageWikiLink Relevance_vector_machine.
• Support_vector_machine wikiPageWikiLink Scikit-learn.

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