FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2095374884> ?p ?o ?g. }

• W2095374884 abstract "We introduce and investigate a new model of learning probability distributions from independent draws. Our model is inspired by the popular Probably Approximately Correct (PAC) model for learning boolean functions from labeled examples [24], in the sense that we emphasize efficient and approximate learning, and we study the learnability of restricted classes of target distributions. The dist ribut ion classes we examine are often defined by some simple computational mechanism for transforming a truly random string of input bits (which is not visible to the learning algorithm) into the stochastic observation (output) seen by the learning algorithm. In this paper, we concentrate on discrete distributions over {O, I}n. The problem of inferring an approximation to an unknown probability distribution on the basis of independent draws has a long and complex history in the pattern recognition and statistics literature. For instance, the problem of estimating the parameters of a Gaussian density in highdimensional space is one of the most studied statistical problems. Distribution learning problems have often been investigated in the context of unsupervised learning, in which a linear mixture of two or more distributions is generating the observations, and the final goal is not to model the distributions themselves, but to predict from which distribution each observation was drawn. Data clustering methods are a common tool here. There is also a large literature on nonpararnetric density estimation, in which no assumptions are made on the unknown target density. Nearest-neighbor approaches to the unsupervised learning problem often arise in the nonparametric setting. While we obviously cannot do justice to these areas here, the books of Duda and Hart [9] and Vapnik [25] provide excellent overviews and introductions to the pattern recognition work, as well as many pointers for further reading. See also Izenman’s recent survey article [16]. Roughly speaking, our work departs from the traditional statistical and pattern recognition approaches in two ways. First, we place explicit emphasis on the comput ationrd complexity of distribution learning. It seems fair to say that while previous research has provided an excellent understanding of the information-theoretic issues involved in dis-" @default.
• W2095374884 created "2016-06-24" @default.
• W2095374884 creator A5014364130 @default.
• W2095374884 creator A5014637159 @default.
• W2095374884 creator A5029730907 @default.
• W2095374884 creator A5041567023 @default.
• W2095374884 creator A5063746987 @default.
• W2095374884 creator A5084108666 @default.
• W2095374884 date "1994-01-01" @default.
• W2095374884 modified "2023-10-06" @default.
• W2095374884 title "On the learnability of discrete distributions" @default.
• W2095374884 cites W1530699444 @default.
• W2095374884 cites W1856342626 @default.
• W2095374884 cites W1968998685 @default.
• W2095374884 cites W2001331793 @default.
• W2095374884 cites W2019363670 @default.
• W2095374884 cites W2070902649 @default.
• W2095374884 cites W2072631813 @default.
• W2095374884 cites W2081585469 @default.
• W2095374884 cites W2084310470 @default.
• W2095374884 cites W2084544490 @default.
• W2095374884 cites W2085759780 @default.
• W2095374884 cites W2088776358 @default.
• W2095374884 cites W2093717447 @default.
• W2095374884 cites W2107993699 @default.
• W2095374884 cites W2115826669 @default.
• W2095374884 cites W2117362057 @default.
• W2095374884 cites W2129192653 @default.
• W2095374884 cites W2141620354 @default.
• W2095374884 cites W2142399242 @default.
• W2095374884 cites W2154952480 @default.
• W2095374884 cites W2157054705 @default.
• W2095374884 cites W2157526632 @default.
• W2095374884 cites W2173249896 @default.
• W2095374884 cites W3017143921 @default.
• W2095374884 cites W5594912 @default.
• W2095374884 doi "https://doi.org/10.1145/195058.195155" @default.
• W2095374884 hasPublicationYear "1994" @default.
• W2095374884 type Work @default.
• W2095374884 sameAs 2095374884 @default.
• W2095374884 citedByCount "258" @default.
• W2095374884 countsByYear W20953748842012 @default.
• W2095374884 countsByYear W20953748842013 @default.
• W2095374884 countsByYear W20953748842014 @default.
• W2095374884 countsByYear W20953748842015 @default.
• W2095374884 countsByYear W20953748842016 @default.
• W2095374884 countsByYear W20953748842017 @default.
• W2095374884 countsByYear W20953748842018 @default.
• W2095374884 countsByYear W20953748842019 @default.
• W2095374884 countsByYear W20953748842020 @default.
• W2095374884 countsByYear W20953748842021 @default.
• W2095374884 countsByYear W20953748842022 @default.
• W2095374884 crossrefType "proceedings-article" @default.
• W2095374884 hasAuthorship W2095374884A5014364130 @default.
• W2095374884 hasAuthorship W2095374884A5014637159 @default.
• W2095374884 hasAuthorship W2095374884A5029730907 @default.
• W2095374884 hasAuthorship W2095374884A5041567023 @default.
• W2095374884 hasAuthorship W2095374884A5063746987 @default.
• W2095374884 hasAuthorship W2095374884A5084108666 @default.
• W2095374884 hasBestOaLocation W20953748842 @default.
• W2095374884 hasConcept C154945302 @default.
• W2095374884 hasConcept C2777723229 @default.
• W2095374884 hasConcept C41008148 @default.
• W2095374884 hasConceptScore W2095374884C154945302 @default.
• W2095374884 hasConceptScore W2095374884C2777723229 @default.
• W2095374884 hasConceptScore W2095374884C41008148 @default.
• W2095374884 hasLocation W20953748841 @default.
• W2095374884 hasLocation W20953748842 @default.
• W2095374884 hasOpenAccess W2095374884 @default.
• W2095374884 hasPrimaryLocation W20953748841 @default.
• W2095374884 hasRelatedWork W180475243 @default.
• W2095374884 hasRelatedWork W1973394166 @default.
• W2095374884 hasRelatedWork W2098466205 @default.
• W2095374884 hasRelatedWork W2748952813 @default.
• W2095374884 hasRelatedWork W2766040214 @default.
• W2095374884 hasRelatedWork W2899084033 @default.
• W2095374884 hasRelatedWork W2951285859 @default.
• W2095374884 hasRelatedWork W37360785 @default.
• W2095374884 hasRelatedWork W4302791369 @default.
• W2095374884 hasRelatedWork W72092541 @default.
• W2095374884 isParatext "false" @default.
• W2095374884 isRetracted "false" @default.
• W2095374884 magId "2095374884" @default.
• W2095374884 workType "article" @default.