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The paper introduces an efficient construction algorithm for obtaining sparse linear-in-the-weights regression models based on an approach of directly optimizing model generalization capability. This is achieved by utilizing the delete-1 cross validation concept and the associated leave-one-out test error also known as the predicted residual sums of squares(More)
User interaction within a virtual environment may take various forms: a teleconferencing application will require users to speak to each other (Geak, 1993), with computer supported cooperative working; an Engineer may wish to pass an object to another user for examination; in a battle field simulation (McDonough, 1992), users might exchange fire. In all(More)
—In this correspondence new robust nonlinear model construction algorithms for a large class of linear-in-the-parameters models are introduced to enhance model robustness via combined parameter regularization and new robust structural selective criteria. In parallel to parameter regularization, we use two classes of robust model selection criteria based on(More)
The introduction of definite and simple action goals into machine vision at an early stage promises to address the defects which have become apparent in the data-driven reconstruc-tionist paradigm, with its relentless emphasis on accurate geometric recovery of the whole 3D scene — a reconstruction which is often unnecessary and usually too difficult. Simple(More)
This paper introduces a new robust nonlinear identification algorithm using the predicted residual sums of squares (PRESS) statistic and forward regression. The major contribution is to compute the PRESS statistic within a framework of a forward orthogonalization process and hence construct a model with a good generalization property. Based on the(More)