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

-imum margin does not necessarily lead to better general-ization performances, and instead, the margin distribution has been proven to be more crucial. In this paper, we pro-pose the Large margin Distribution Machine (LDM), which tries to achieve a better generalization performance by opti-mizing the margin

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

this paper that minimising this new criterion can be performed efficiently.

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

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 mar-gin mean and minimizing the margin

We study the properties of marginal distributions-projections of the phase space representation of a physical system-under relativistic transforms. We consider the Galileo case as well as the Lorentz transforms exploiting the relativistic oscillator model used for describing the mass spectrum

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

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

to accomplish this, we propose to appropriately generalize the well-known notion of a separation margin and derive a corresponding maximum-margin formulation. While this leads to a quadratic program with a potentially prohibitive, i.e. exponential, number of constraints, we present a cutting plane algorithm