Based on biological data we examine the ability of Support Vector Machines (SVMs) with gaussian kernels to learn and predict the nonlinear dynamics of single biological neurons. We...
An m-sample semiparametric model in which the ratio of m - 1 probability density functions with respect to the mth is of a known parametric form without reference to any parametri...
Recent work in the field of machine translation (MT) evaluation suggests that sentence level evaluation based on machine learning (ML) can outperform the standard metrics such as B...
Antoine Veillard, Elvina Melissa, Cassandra Theodo...
Selection of an optimal estimator typically relies on either supervised training samples (pairs of measurements and their associated true values), or a prior probability model for...
We present a new definition of an implicit surface over a noisy point cloud, based on the weighted least squares approach. It can be evaluated very fast, but artifacts are signifi...