Zobrazeno 1 - 10
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pro vyhledávání: '"Vapnik–Chervonenkis Theory"'
Akademický článek
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Autor:
Saptarshi Chakraborty, Swagatam Das
Publikováno v:
Statistics & Probability Letters. 175:109102
Bi-clustering refers to the task of partitioning the rows and columns of a data matrix simultaneously. Although empirically useful, the theoretical aspects of bi-clustering techniques have not been studied in-depth. We present a framework for investi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1677ce32b764f4c9b2d55eee8feb7440
https://doi.org/10.1016/j.spl.2021.109102
https://doi.org/10.1016/j.spl.2021.109102
Publikováno v:
Data-Enabled Discovery and Applications. 1
The proposed work assesses the composed mixture of machine learning algorithms(MLA) mentioned as principal component analysis (PCA) and kernel principal component analysis (KPCA) along with vapnik chervonenkis theory (VCT) on the datasets. The main h
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0cda62a5bd9cb6c8fd6605e94658583a
https://doi.org/10.1007/s41688-017-0006-x
https://doi.org/10.1007/s41688-017-0006-x
Autor:
Alexander Rakhlin, Karthik Sridharan
Publikováno v:
Measures of Complexity ISBN: 9783319218519
We review recent advances on uniform martingale laws of large numbers and the associated sequential complexity measures. These results may be considered as forming a non-i.i.d. generalization of Vapnik–Chervonenkis theory. We discuss applications t
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https://explore.openaire.eu/search/publication?articleId=doi_________::6282d6b558d53ac6629e56faddf5ffb9
https://doi.org/10.1007/978-3-319-21852-6_15
https://doi.org/10.1007/978-3-319-21852-6_15
Publikováno v:
A Probabilistic Theory of Pattern Recognition ISBN: 9781461268772
In this section we list a few interesting properties of shatter coefficient s(A, n) and of the vc dimension V A of a class of sets A. We begin with a property that makes things easier.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0cd55520b8d2da55ca8a69d0d92cc499
https://doi.org/10.1007/978-1-4612-0711-5_13
https://doi.org/10.1007/978-1-4612-0711-5_13
Publikováno v:
A Probabilistic Theory of Pattern Recognition ISBN: 9781461268772
In this chapter we select a decision rule from a class of rules with the help of training data. Working formally, let C be a class of functions O: R d → {0,1}. One wishes to select a function from C with small error probability. Assume that the tra
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https://explore.openaire.eu/search/publication?articleId=doi_________::9433c67b34c38f8a5386450616fb2d75
https://doi.org/10.1007/978-1-4612-0711-5_12
https://doi.org/10.1007/978-1-4612-0711-5_12
Kniha
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Autor:
Peter Gaenssler, Winfried Stute
Publikováno v:
DMV Seminar ISBN: 9783764319212
As always, let ξ1,ξ2,...be a sequence of i.i.d random variables with common law μ, defined on the Borel σ-field B in ϰ=ℝ, and let μn be the empirical measure based on ξ1,...,ξn, i.e. $${\mu _n}\left( {\text{B}} \right): = {{\text{n}}^{ - 1}
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bd34c508967bfc3981c799003ff9c621
https://doi.org/10.1007/978-3-0348-6269-1_9
https://doi.org/10.1007/978-3-0348-6269-1_9
Autor:
Nobel, Andrew
Publikováno v:
The Annals of Statistics, 1996 Jun 01. 24(3), 1084-1105.
Externí odkaz:
https://www.jstor.org/stable/2242583