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VC Dimension
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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 can be wiggly, so it can fit a given set of training points well. But one can expect that the classifier will make errors on other points, because it is too wiggly. Such a polynomial has a high capacity. A much simpler alternative is to threshold a linear function. This polynomial may not fit the training set well, because it has a low capacity. We make this notion of capacity more rigorous below.
Weitere Informationen
- Allgemeine Informationen
- GTIN 09786131124051
- Editor Lambert M. Surhone, Miriam T. Timpledon, Susan F. Marseken
- EAN 9786131124051
- Format Fachbuch
- Titel VC Dimension
- Herausgeber Betascript Publishing
- Anzahl Seiten 68
- Genre Mathematik
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