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- Sample_complexity abstract "In machine learning, the sample complexity of a machine learning algorithm is, roughly speaking, the number of training samples needed for the algorithm to successfully learn a target function. More specifically, the sample complexity is the number of samples needed for the function returned by the algorithm to be within an arbitrarily small error of the best possible function, with probability arbitrarily close to 1.There are two variants of sample complexity. The weak variant fixes a particular input-output distribution, and the strong variant takes the worst-case sample complexity over all input-output distributions. A natural question in statistical learning is to ask, for a given hypothesis space, whether the sample complexity is finite in the strong sense, that is, there is a bound on the number of samples needed so that an algorithm can approximately solve all possible learning problems on a particular input-output space no matter the distribution of data over that space. The No Free Lunch Theorem, discussed below, says that this is always impossible if the hypothesis space is not constrained.".
- Sample_complexity wikiPageID "43269516".
- Sample_complexity wikiPageLength "9669".
- Sample_complexity wikiPageOutDegree "15".
- Sample_complexity wikiPageRevisionID "706262458".
- Sample_complexity wikiPageWikiLink Active_learning.
- Sample_complexity wikiPageWikiLink Category:Machine_learning.
- Sample_complexity wikiPageWikiLink Glivenko–Cantelli_theorem.
- Sample_complexity wikiPageWikiLink Machine_learning.
- Sample_complexity wikiPageWikiLink No_free_lunch_in_search_and_optimization.
- Sample_complexity wikiPageWikiLink Online_machine_learning.
- Sample_complexity wikiPageWikiLink Probably_approximately_correct_learning.
- Sample_complexity wikiPageWikiLink Rademacher_complexity.
- Sample_complexity wikiPageWikiLink Regularization_(mathematics).
- Sample_complexity wikiPageWikiLink Reinforcement_learning.
- Sample_complexity wikiPageWikiLink Semi-supervised_learning.
- Sample_complexity wikiPageWikiLink Tikhonov_regularization.
- Sample_complexity wikiPageWikiLink VC_dimension.
- Sample_complexity wikiPageWikiLink Vapnik–Chervonenkis_theory.
- Sample_complexity wikiPageWikiLinkText "sample complexity".
- Sample_complexity wikiPageUsesTemplate Template:Machine_learning_bar.
- Sample_complexity wikiPageUsesTemplate Template:Math.
- Sample_complexity wikiPageUsesTemplate Template:Reflist.
- Sample_complexity subject Category:Machine_learning.
- Sample_complexity comment "In machine learning, the sample complexity of a machine learning algorithm is, roughly speaking, the number of training samples needed for the algorithm to successfully learn a target function. More specifically, the sample complexity is the number of samples needed for the function returned by the algorithm to be within an arbitrarily small error of the best possible function, with probability arbitrarily close to 1.There are two variants of sample complexity.".
- Sample_complexity label "Sample complexity".
- Sample_complexity sameAs Q18354077.
- Sample_complexity sameAs m.0114dpwp.
- Sample_complexity sameAs Q18354077.
- Sample_complexity wasDerivedFrom Sample_complexity?oldid=706262458.
- Sample_complexity isPrimaryTopicOf Sample_complexity.