Results 221 to 230 of about 185,569 (262)
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1997
The traditional definition of rate of convergence refers to the asymptotic properties of normalized errors about the limit point \( \bar \theta \). If e n = e for the Robbins—Monro algorithm, it is concerned with the asymptotic properties of \( U_n^ \in = \left( {\theta _n^ \in - \bar \theta } \right)/\sqrt \in \) for large n and small ∈.
Harold J. Kushner, G. George Yin
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The traditional definition of rate of convergence refers to the asymptotic properties of normalized errors about the limit point \( \bar \theta \). If e n = e for the Robbins—Monro algorithm, it is concerned with the asymptotic properties of \( U_n^ \in = \left( {\theta _n^ \in - \bar \theta } \right)/\sqrt \in \) for large n and small ∈.
Harold J. Kushner, G. George Yin
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The convergence rate of the MDM algorithm
The 2012 International Joint Conference on Neural Networks (IJCNN), 2012In this paper we will describe a simple proof of a linear convergence rate for the MDM algorithm that solves the Minimum Norm Problem (MNP). Linear convergence rates have been shown for the SMO algorithm, but the proofs require specific assumptions and are rather involved. We will follow a different approach, with a more geometric flavor.
Jorge López Lázaro +1 more
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1978
In Section 7.1, rate of convergence is defined, and our approach to the rate problem discussed. The rates are developed (in Section 7.3) for three separate cases, two forms of the basic KW procedure and the basic RM procedure. These algorithms are discussed in Section 7.1 and are put into a form which will be useful in the subsequent development.
Harold J. Kushner, Dean S. Clark
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In Section 7.1, rate of convergence is defined, and our approach to the rate problem discussed. The rates are developed (in Section 7.3) for three separate cases, two forms of the basic KW procedure and the basic RM procedure. These algorithms are discussed in Section 7.1 and are put into a form which will be useful in the subsequent development.
Harold J. Kushner, Dean S. Clark
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Convergence rate on periodic gossiping
Information Sciences, 2016zbMATH Open Web Interface contents unavailable due to conflicting licenses.
F. He, S. Mou, J. Liu, A.S. Morse
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2015
In this chapter, we study the local rate of convergence of r n (x) to r(x). We obtain full information on the first asymptotic term of r n (x) − r(x), and are rewarded with (i) a central limit theorem for r n (x) − r(x), and (ii) a way of helping the user decide how to choose the weights v ni of the estimate.
Gérard Biau, Luc Devroye
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In this chapter, we study the local rate of convergence of r n (x) to r(x). We obtain full information on the first asymptotic term of r n (x) − r(x), and are rewarded with (i) a central limit theorem for r n (x) − r(x), and (ii) a way of helping the user decide how to choose the weights v ni of the estimate.
Gérard Biau, Luc Devroye
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1996
This chapter gives some results on rates of convergence of M-estimators, including maximum likelihood estimators and least-squares estimators. We first state an abstract result, which is a generalization of the theorem on rates of convergence in Chapter 3.2, and next discuss some methods to establish the maximal inequalities needed for the application ...
Aad W. van der Vaart, Jon A. Wellner
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This chapter gives some results on rates of convergence of M-estimators, including maximum likelihood estimators and least-squares estimators. We first state an abstract result, which is a generalization of the theorem on rates of convergence in Chapter 3.2, and next discuss some methods to establish the maximal inequalities needed for the application ...
Aad W. van der Vaart, Jon A. Wellner
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Global convergence and rate of convergence of a method of centers
Computational Optimization and Applications, 1994zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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On the Convergence Rate in the Invariance Principle
Theory of Probability & Its Applications, 1985Let H be a real separable Hilbert space. An estimate of the convergence rate in the invariance principle for H-valued random variables is obtained. The sequence of coordinates of the random variables is supposed to be martingale-difference.
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Exponential Convergence Rates in Classification
2005Let (X,Y) be a random couple, X being an observable instance and Y∈ {–1,1} being a binary label to be predicted based on an observation of the instance. Let (Xi, Yi), i=1, . . . , n be training data consisting of n independent copies of (X,Y). Consider a real valued classifier ${\hat{f}_{n}}$ that minimizes the following penalized empirical risk ...
Vladimir Koltchinskii +1 more
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THE RATE OF RESOLVENT AND SEMIGROUP CONVERGENCE
The Quarterly Journal of Mathematics, 1984In this paper the author generalizes and sharpens some inequalities concerning the norm of differences \(\| S(t)-T(t)\|\) of semigroups S(t), T(t) respectively, of operators on Banach spaces. Moreover estimates of the differences of the resolvents of the associated infinitesimal generators are included.
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