Matches in DBpedia 2015-10 for { <http://dbpedia.org/resource/VC_dimension> ?p ?o }
Showing triples 1 to 79 of
79
with 100 triples per page.
- VC_dimension abstract "In statistical learning theory, or sometimes computational learning theory, the VC dimension (for Vapnik–Chervonenkis dimension) is a measure of the capacity (complexity, expressive power, richness, or flexibility) 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 function may not fit the training set well, because it has a low capacity. This notion of capacity is made rigorous below.".
- VC_dimension thumbnail VC1.svg?width=300.
- VC_dimension wikiPageExternalLink book.html.
- VC_dimension wikiPageExternalLink burges98tutorial.html.
- VC_dimension wikiPageExternalLink page-1.
- VC_dimension wikiPageExternalLink vcdim.html.
- VC_dimension wikiPageExternalLink S002200009791477X.
- VC_dimension wikiPageID "305846".
- VC_dimension wikiPageLength "6270".
- VC_dimension wikiPageOutDegree "35".
- VC_dimension wikiPageRevisionID "682803578".
- VC_dimension wikiPageWikiLink Alexey_Chervonenkis.
- VC_dimension wikiPageWikiLink Algorithm.
- VC_dimension wikiPageWikiLink Bernard_Chazelle.
- VC_dimension wikiPageWikiLink Cardinality.
- VC_dimension wikiPageWikiLink Category:Computational_learning_theory.
- VC_dimension wikiPageWikiLink Category:Dimension.
- VC_dimension wikiPageWikiLink Category:Measures_of_complexity.
- VC_dimension wikiPageWikiLink Category:Statistical_classification.
- VC_dimension wikiPageWikiLink Computational_geometry.
- VC_dimension wikiPageWikiLink Computational_learning_theory.
- VC_dimension wikiPageWikiLink Degree_of_a_polynomial.
- VC_dimension wikiPageWikiLink E-net_(computational_geometry).
- VC_dimension wikiPageWikiLink Heaviside_step_function.
- VC_dimension wikiPageWikiLink Independent_and_identically_distributed_random_variables.
- VC_dimension wikiPageWikiLink Independent_identically-distributed_random_variables.
- VC_dimension wikiPageWikiLink Kernel_method.
- VC_dimension wikiPageWikiLink Kernel_methods.
- VC_dimension wikiPageWikiLink Linear_classifier.
- VC_dimension wikiPageWikiLink Manfred_K._Warmuth.
- VC_dimension wikiPageWikiLink Natarajan_dimension.
- VC_dimension wikiPageWikiLink Perceptron.
- VC_dimension wikiPageWikiLink Polynomial.
- VC_dimension wikiPageWikiLink Probabilistic.
- VC_dimension wikiPageWikiLink Probability.
- VC_dimension wikiPageWikiLink Rademacher_complexity.
- VC_dimension wikiPageWikiLink Radons_theorem.
- VC_dimension wikiPageWikiLink Sauer–Shelah_lemma.
- VC_dimension wikiPageWikiLink Shattered_set.
- VC_dimension wikiPageWikiLink Shattering_(machine_learning).
- VC_dimension wikiPageWikiLink Statistical_classification.
- VC_dimension wikiPageWikiLink Upper_and_lower_bounds.
- VC_dimension wikiPageWikiLink Upper_bound.
- VC_dimension wikiPageWikiLink Vapnik–Chervonenkis_theory.
- VC_dimension wikiPageWikiLink Vladimir_Vapnik.
- VC_dimension wikiPageWikiLink Ε-net_(computational_geometry).
- VC_dimension wikiPageWikiLink File:VC1.svg.
- VC_dimension wikiPageWikiLink File:VC2.svg.
- VC_dimension wikiPageWikiLink File:VC3.svg.
- VC_dimension wikiPageWikiLink File:VC4.svg.
- VC_dimension wikiPageWikiLinkText "VC dimension".
- VC_dimension wikiPageWikiLinkText "VC-Dimension".
- VC_dimension wikiPageWikiLinkText "VC-dimension".
- VC_dimension wikiPageWikiLinkText "Vapnik-Chervonenkis dimension".
- VC_dimension wikiPageWikiLinkText "Vapnik–Chervonenkis dimension".
- VC_dimension hasPhotoCollection VC_dimension.
- VC_dimension wikiPageUsesTemplate Template:Commons_category.
- VC_dimension subject Category:Computational_learning_theory.
- VC_dimension subject Category:Dimension.
- VC_dimension subject Category:Measures_of_complexity.
- VC_dimension subject Category:Statistical_classification.
- VC_dimension hypernym Measure.
- VC_dimension type Type.
- VC_dimension type Work.
- VC_dimension type Type.
- VC_dimension comment "In statistical learning theory, or sometimes computational learning theory, the VC dimension (for Vapnik–Chervonenkis dimension) is a measure of the capacity (complexity, expressive power, richness, or flexibility) of a statistical classification algorithm, defined as the cardinality of the largest set of points that the algorithm can shatter.".
- VC_dimension label "VC dimension".
- VC_dimension sameAs Dimensión_VC.
- VC_dimension sameAs Dimension_de_Vapnik-Chervonenkis.
- VC_dimension sameAs ממד_VC.
- VC_dimension sameAs Wymiar_Wapnika-Czerwonienkisa.
- VC_dimension sameAs m.01shr2.
- VC_dimension sameAs Размерность_Вапника_—_Червоненкиса.
- VC_dimension sameAs Chiều_VC.
- VC_dimension sameAs Q2662236.
- VC_dimension sameAs Q2662236.
- VC_dimension wasDerivedFrom VC_dimension?oldid=682803578.
- VC_dimension depiction VC1.svg.
- VC_dimension isPrimaryTopicOf VC_dimension.