VC Dimension
Lambert M. Surhone, Miriam T. Timpledon, Susan F. Marseken
High Quality Content by WIKIPEDIA articles! In statistical learning theory, or sometimes computational learning theory, the VC dimension (for Vapnik–Chervonenkis dimension) is a measure of the capacity of a statistical classification algorithm, defined as the cardinality of the largest set of points that the algorithm can shatter. It is a core concept in Vapnik–Chervonenkis theory, and was originally defined by Vladimir Vapnik and Alexey Chervonenkis. Informally, the capacity of a classification model is related to how complicated it can be. For example, consider the thresholding of a high-degree polynomial: if the polynomial evaluates above zero, that point is classified as positive, otherwise as negative. A high-degree polynomial...
ISBN: 978-6-1311-2405-1
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Книга по требованию
Дата выхода: июль 2011