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Entropy Numbers of Certain Summation Operators
gmj, 2001Abstract Given nonnegative real sequences and we study the generated summation operator regarded as a mapping from ℓ p (ℤ) to ℓ q (ℤ).
Creutzig, J., Linde, W.
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The entropy source of pseudo random number generators: from low entropy to high entropy
2019 IEEE International Conference on Intelligence and Security Informatics (ISI), 2019The pseudo random number generators (PRNG) is one type of deterministic functions. The information entropy of the output sequences depends on the entropy of the input seeds. The output sequences can be predicted if attackers could know or control the input seeds of PRNGs.
Jizhi Wang, Jingshan Pan, Xueli Wu
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Entropy numbers and interpolation
Mathematische Nachrichten, 1982From the author's introduction. It is the purpose of this note to answer a query put forward by \textit{H. Triebel} in [Interpolation theory, function spaces, differential operators (1978; Zbl 0387.46032), p. 118], regarding interpolation properties of a certain class of operator ideals, the so-called entropy ideals.
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Some estimates on entropy numbers
Israel Journal of Mathematics, 1993zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Junge, Marius, Defant, Martin
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Entropy-Based Random Number Evaluation
American Journal of Mathematical and Management Sciences, 1995SYNOPTIC ABSTRACTPrevious work has shown how to test a simple hypothesis of uniformity on the interval (0, 1) by using spacings-based estimates of entropy. In this paper we use Monte Carlo methods to extend previous tables of critical points and power for such entropy tests to the large sample sizes likely to be desirable when evaluating the output of ...
Edward J. Dudewicz +3 more
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Entropy estimation and Fibonacci numbers
SPIE Proceedings, 2013We introduce a new metric on a space of right-sided infinite sequences drawn from a finite alphabet. Emerging from a problem of entropy estimation of a discrete stationary ergodic process, the metric is important on its own part and exhibits some interesting properties.
Evgeniy A. Timofeev, Alexei Kaltchenko
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Mathematische Nachrichten, 1987
The authors study generalized inner entropy numbers \(f_ n(T,{\mathfrak A})\) and Gelfand numbers \(c_ n(T,{\mathfrak A})\) of an operator T relative to some operator ideal \({\mathfrak A}\). Denoting the ideal of those maps T for which \((f_ n(T({\mathfrak A}))_{n\in {\mathbb{N}}}\) belongs to the Lorentz sequence space \(\ell_{p,q}\) by \({\mathfrak ...
Carl, Bernd, Stephani, Irmtraud
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The authors study generalized inner entropy numbers \(f_ n(T,{\mathfrak A})\) and Gelfand numbers \(c_ n(T,{\mathfrak A})\) of an operator T relative to some operator ideal \({\mathfrak A}\). Denoting the ideal of those maps T for which \((f_ n(T({\mathfrak A}))_{n\in {\mathbb{N}}}\) belongs to the Lorentz sequence space \(\ell_{p,q}\) by \({\mathfrak ...
Carl, Bernd, Stephani, Irmtraud
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The Entropy Generation Minimisation based on the Revised Entropy Generation Number
International Journal of Exergy, 2010In the present work, an improved Entropy Generation Minimisation (EGM) approach aiming at minimising the revised entropy generation number which is the non-dimensionalised entropy by the ratio of heat flow to the input temperature of the cold fluid is developed for a plate-fin heat exchanger design with multiple design variables with the help of ...
Jiangfeng Guo, Lin Cheng, Mingtian Xu
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Entropy Numbers, Approximation Numbers, and Embeddings
1998For convenience, all spaces considered here will be over the complex field. Given any Banach spaces X and Y, L (X, Y) will stand for the space of all bounded linear maps from X to Y; we shall write L(X) instead of L(X, X). We also put B X = {x ∈ X : ‖x‖ ≤ 1}. Let T ∈ L(X, Y) and n ∈ ℕ.
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Entropy Numbers, Operators and Support Vector Kernels
1998We derive new bounds for the generalization error of feature space machines, such as support vector machines and related regularization networks by obtaining new bounds on their covering numbers. The proofs are based on a viewpoint that is apparently novel in the field of statistical learning theory.
Williamson, R. +2 more
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