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SUPPORT VECTOR MACHINEEXAMPLES WITH MATLAB
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Sinopsis
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 as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to 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.
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Ficha Técnica
Editorial: Autor-editor
ISBN: cdlap00011301
Idioma: Inglés
Fecha de lanzamiento: 07/01/2019
Especificaciones del producto
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