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In addition to handling the relationship between observed data and the latent trait via the link and distribution functions, any system for expected and observed scale quantitative

In the state of the art, the weighted sum of scores (which is a linear classifier) and the use of an SVM (which is a non linear classifier) pro- vided by different biometric

In [5], we exposed a review of sampling methods used with GP. Mainly, sampling methods can be classified with regard to three properties: re-sampling frequency, sampling scheme

Selection of the Best Genetic Programming-Implied Volatility Forecasting Models Selection of the best generated GP volatility model, relative to each training set, for

Support Vector Machines (SVMs) are well-established Ma- chine Learning (ML) algorithms. They rely on the fact that i) linear learning can be formalized as a well-posed

In this article we present a new GP algorithm called Zoetrope Genetic Programming (ZGP), which brings the following main contributions: (1) a new and unseen rep- resentation of

Animal model for heterogeneous additive genetic variances To allow the animal model to account for potentially heterogeneous additive genetic variances of different base

In order to achieve this goal, we use algebraic methods developed in constructive algebra, D-modules (differential modules) theory, and symbolic computation such as the so-called