Results 271 to 280 of about 5,052,793 (311)

Margin Distribution Bounds on Generalization

1999
A number of results have bounded generalization of a classi fier in terms of its margin on the training points. There has been some debate about whether the minimum margin is the best measure of the distribution of training set margin values with which to estimate the generalization.
John Shawe-Taylor, Nello Cristianini
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Large Margin Distribution Learning

2014
Support vector machines (SVMs) and Boosting are possibly the two most popular learning approaches during the past two decades. It is well known that the margin is a fundamental issue of SVMs, whereas recently the margin theory for Boosting has been defended, establishing a connection between these two mainstream approaches.
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Bounds on margin distributions inBlearningBproblems

Annales de l?Institut Henri Poincare (B) Probability and Statistics, 2003
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Multitaper marginal time–frequency distributions

Signal Processing, 2006
Time-frequency distributions (TFDs) belonging to Cohen's class yield a frequency marginal that is equivalent to the periodogram of the signal. It is well-known that the periodogram is not a good spectral estimator since it is not a consistent estimate, i.e. its variance does not decrease with the sample size. Thomson addressed this issue by introducing
Selin Aviyente, William J. Williams
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A Reformulation of the Marginal Productivity Theory of Distribution

Econometrica, 1984
Reformulating marginal productivity theory by replacing productivity with respect to commodities with productivity with respect to persons and then defining perfectly competitive equilibrium as an allocation at which each person receives the marginal product of his/her contribution - called a no-surplus allocation - there emerges a competitive theory ...
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Operator ? Stable distributions with independent marginals

Zeitschrift f�r Wahrscheinlichkeitstheorie und Verwandte Gebiete, 1981
Let Μ be a full operator-stable probability measure over a finite dimensional real inner product space V. Necessary and sufficient conditions are obtained for Μ to have independent univariate marginals with respect to some basis of V. These essentially amount to the statement that the support of the Levy spectral measure is a subset of the union of one-
Hudson, William N., Mason, J. David, Tucker, Howard G.   +2 more
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Multiview Large Margin Distribution Machine

IEEE Transactions on Neural Networks and Learning Systems
Margin distribution has been proven to play a crucial role in improving generalization ability. In recent studies, many methods are designed using large margin distribution machine (LDM), which combines margin distribution with support vector machine (SVM), such that a better performance can be achieved.
Kun Hu, Yingyuan Xiao, Wenguang Zheng, Wenxin Zhu, Ching-Hsien Hsu   +4 more
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On the Existence of Probability Distributions with Given Marginals

Theory of Probability & Its Applications, 2004
Summary: Let \(X=\{0,\ldots, n-1\}\) and \(\Gamma=\{(x_1,\ldots, x_s)\in X^s:\,\sum_{\sigma=1}^s x_\sigma=n-1\}\). For the marginals of probability distributions on \(\Gamma\) with the additional property of forming an \(s\)-tuple of decreasing probabilities on \(X\) a simple characterization is given.
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A Bivariate Marginal Distribution Genetic Model

2006 IEEE International Conference on Evolutionary Computation, 2006
We introduce a genetic model based on simulated crossover of fixed sequences of two bit genes. States and dynamics of the deterministic genetic system, represented by the model , are derived in the case of infinite populations and for finite fitness functions (expressed in terms of multivariate polynomials).
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A Note on Multivariate Distributions with Specified Marginals

Journal of the Operational Research Society, 1988
Johnson and Tenenbein describe a procedure for generating random values from a bivariate distribution with specified marginal forms and any required correlation between the two variables. This note shows that the approach can be extended to produce values from general multivariate distributions in a straightforward manner.
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