Sparse modeling using orthogonal forward regression with PRESS statistic and regularization

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 (PRESS) statistic, without resorting to any other validation data set for model evaluation in the model construction process. Computational efficiency is ensured using an orthogonal forward regression, but the algorithm incrementally minimizes the PRESS statistic instead of the usual sum of the squared training errors. A local regularization method can naturally be incorporated into the model selection procedure to further enforce model sparsity. The proposed algorithm is fully automatic, and the user is not required to specify any criterion to terminate the model construction procedure. Comparisons with some of the existing state-of-art modeling methods are given, and several examples are included to demonstrate the ability of the proposed algorithm to effectively construct sparse models that generalize well.

A robust nonlinear identification algorithm using PRESS statistic and forward regression

This letter introduces a new robust nonlinear identification algorithm using the Predicted REsidual Sums of Squares (PRESS) statistic and for-ward 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 properties of the PRESS statistic the proposed algorithm can achieve a fully automated procedure without resort to any other validation data set for iterative model evaluation.

Automatic nonlinear predictive model-construction algorithm using forward regression and the PRESS statistic

An automatic nonlinear predictive model-construction algorithm is introduced based on forward regression and the predicted-residual-sums-of-squares (PRESS) statistic. The proposed algorithm is based on the fundamental concept of evaluating a model's generalisation capability through crossvalidation. This is achieved by using the PRESS statistic as a cost function to optimise model structure. In particular, the proposed algorithm is developed with the aim of achieving computational efficiency, such that the computational effort, which would usually be extensive in the computation of the PRESS statistic, is reduced or minimised. The computation of PRESS is simplified by avoiding a matrix inversion through the use of the orthogonalisation procedure inherent in forward regression, and is further reduced significantly by the introduction of a forward-recursive formula. Based on the properties of the PRESS statistic, the proposed algorithm can achieve a fully automated procedure without resort to any other validation data set for iterative model evaluation. Numerical examples are used to demonstrate the efficacy of the algorithm.

Virginia Woolf, Hugh Walpole, the Hogarth Press, and the Book Society

This article explores the unlikely relationship and alliance between the novelists Virginia Woolf and Hugh Walpole. It examines the ways in which these typically highbrow and middlebrow writers influenced each others’ lives and work, and focuses in particular on the interactions between the Woolfs’ Hogarth Press and Walpole’s Book Society, the first book club to operate in Great Britain. The article uses a number of case studies drawn from the Hogarth Press archives to demonstrate how by the 1930s, the Hogarth Press was much more commercial in its operations and pursuits of reading markets than is often recognized.

The Press in a world of technological change, 1970–2000

Oxford University Press’s response to technological change in printing and publishing processes in this period can be considered in three phases: an initial period when the computerization of typesetting was seen as offering both cost savings and the ability to produce new editions of existing works more quickly; an intermediate phase when the emergence of standards in desktop computing allowed experiments with the sale of software as well as packaged electronic publications; and a third phase when the availability of the world wide web as a means of distribution allowed OUP to return to publishing in its traditional areas of strength albeit in new formats. Each of these phases demonstrates a tension between a desire to develop centralized systems and expertise, and a recognition that dynamic publishing depends on distributed decision-making and innovation. Alongside these developments in production and distribution lay developments in computer support for managerial and collaborative publishing processes, often involving the same personnel and sometimes the same equipment.