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8,284
The Bivariate Marginal Distribution Algorithm
, 1999
"... The paper deals with the Bivariate Marginal Distribution Algorithm (BMDA). BMDA is an extension of the Univariate Marginal Distribution Algorithm (UMDA). It uses the pair gene dependencies in order to improve algorithms that use simple univariate marginal distributions. BMDA is a special case of the ..."
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Cited by 114 (22 self)
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The paper deals with the Bivariate Marginal Distribution Algorithm (BMDA). BMDA is an extension of the Univariate Marginal Distribution Algorithm (UMDA). It uses the pair gene dependencies in order to improve algorithms that use simple univariate marginal distributions. BMDA is a special case
Large Margin Distribution Machine
"... Support vector machine (SVM) has been one of the most popular learning algorithms, with the central idea of maximizing the minimum margin, i.e., the smallest distance from the instances to the classification boundary. Recent theoretical results, however, disclosed that maximizing the minimum marg ..."
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Cited by 1 (1 self)
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imum margin does not necessarily lead to better generalization performances, and instead, the margin distribution has been proven to be more crucial. In this paper, we propose the Large margin Distribution Machine (LDM), which tries to achieve a better generalization performance by optimizing the margin
Further Results on the Margin Distribution
 In Proc. 12th Annu. Conf. on Comput. Learning Theory
, 1999
"... A number of results have bounded generalization of a classifier 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. Freund and Schap ..."
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Cited by 35 (9 self)
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A number of results have bounded generalization of a classifier 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. Freund
Margin Distribution and Soft Margin
, 1999
"... this paper that minimising this new criterion can be performed efficiently. ..."
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Cited by 16 (3 self)
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this paper that minimising this new criterion can be performed efficiently.
Large Margin Distribution Learning
"... Abstract. 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 betw ..."
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between these two mainstream approaches. The recent theoretical results disclosed that the margin distribution rather than a single margin is really crucial for the generalization performance, and suggested to optimize the margin distribution by maximizing the margin mean and minimizing the margin
Marginal Distributions in Evolutionary Algorithms
 In Proceedings of the International Conference on Genetic Algorithms Mendel ’98
, 1999
"... In this paper, the description of two gene pool recombination operators is described. Both operators are based on the estimation of the distribution of the parents and its use to generate new individuals. The Univariate Marginal Distribution Algorithm (UMDA) uses simple univariate distributions. ..."
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Cited by 12 (0 self)
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In this paper, the description of two gene pool recombination operators is described. Both operators are based on the estimation of the distribution of the parents and its use to generate new individuals. The Univariate Marginal Distribution Algorithm (UMDA) uses simple univariate distributions
Margin Distribution Bounds on Generalization
, 1998
"... A number of results have bounded generalization of a classifier 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. Freund and Schap ..."
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Cited by 16 (4 self)
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A number of results have bounded generalization of a classifier 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. Freund
Relativistic properties of marginal distributions
 Phys. Scr
, 1998
"... We study the properties of marginal distributionsprojections of the phase space representation of a physical systemunder relativistic transforms. We consider the Galileo case as well as the Lorentz transforms exploiting the relativistic oscillator model used for describing the mass spectrum of ele ..."
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Cited by 1 (0 self)
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We study the properties of marginal distributionsprojections of the phase space representation of a physical systemunder relativistic transforms. We consider the Galileo case as well as the Lorentz transforms exploiting the relativistic oscillator model used for describing the mass spectrum
Boosting the margin: A new explanation for the effectiveness of voting methods
 IN PROCEEDINGS INTERNATIONAL CONFERENCE ON MACHINE LEARNING
, 1997
"... One of the surprising recurring phenomena observed in experiments with boosting is that the test error of the generated classifier usually does not increase as its size becomes very large, and often is observed to decrease even after the training error reaches zero. In this paper, we show that this ..."
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Cited by 897 (52 self)
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that this phenomenon is related to the distribution of margins of the training examples with respect to the generated voting classification rule, where the margin of an example is simply the difference between the number of correct votes and the maximum number of votes received by any incorrect label. We show
D.: Margin distribution and learning algorithms
 In: Proceedings of the 12th Conference on Computational Learning Theory
, 1999
"... Recent theoretical results have shown that improved bounds on generalization error of classiers can be obtained by explicitly taking the observed margin distribution of the training data into account. Currently, algorithms used in practice do not make use of the margin distribution and are driven ..."
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Cited by 12 (0 self)
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Recent theoretical results have shown that improved bounds on generalization error of classiers can be obtained by explicitly taking the observed margin distribution of the training data into account. Currently, algorithms used in practice do not make use of the margin distribution
Results 1  10
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8,284