The spatial turn: geographical approaches in the history of science

Over the past decade or so a number of historians of science and historical geographers, alert to the situated nature of scientific knowledge production and reception and to the migratory patterns of science on the move, have called for more explicit treatment of the geographies of past scientific knowledge. Closely linked to work in the sociology of scientific knowledge and science studies and connected with a heightened interest in spatiality evident across the humanities and social sciences this ‹spatial turn’ has informed a wide-ranging body of work on the history of science. This discussion essay revisits some of the theoretical props supporting this turn to space and provides a number of worked examples from the history of the life sciences that demonstrate the different ways in which the spaces of science have been comprehended.

Model selection approaches for non-linear system identification: a review

The identification of non-linear systems using only observed finite datasets has become a mature research area over the last two decades. A class of linear-in-the-parameter models with universal approximation capabilities have been intensively studied and widely used due to the availability of many linear-learning algorithms and their inherent convergence conditions. This article presents a systematic overview of basic research on model selection approaches for linear-in-the-parameter models. One of the fundamental problems in non-linear system identification is to find the minimal model with the best model generalisation performance from observational data only. The important concepts in achieving good model generalisation used in various non-linear system-identification algorithms are first reviewed, including Bayesian parameter regularisation and models selective criteria based on the cross validation and experimental design. A significant advance in machine learning has been the development of the support vector machine as a means for identifying kernel models based on the structural risk minimisation principle. The developments on the convex optimisation-based model construction algorithms including the support vector regression algorithms are outlined. Input selection algorithms and on-line system identification algorithms are also included in this review. Finally, some industrial applications of non-linear models are discussed.