New Ways of Looking at the State Apparatus and the State Archive in Nineteenth Century Ireland: Curiosities from that Phonetic Museum - Royal Irish Constabulary Reports and their Political Uses, 1879-91

This essay seeks to contextualise the intelligence work of the Royal Irish Constabulary, particularly in the 1880s, in terms of the wider British and imperial practice and, as a corollary, to reflect upon aspects of the structure of the state apparatus and the state archive in Ireland since the Union. The author contrasts Irish and British police and bureaucratic work and suggests parallels between Ireland and other imperial locations, especially India. This paper also defines the narrowly political, indeed partisan, uses to which this intelligence was put, particularly during the Special Commission of 1888 on 'Parnellism and crime', when governmentheld police records were made available to counsel for The Times. By reflecting on the structure of the state apparatus and its use in this instance, the author aims to further the debate on the governance of nineteenth-century Ireland and to explore issues of colonial identity and practice. The line of argument proposed in this essay is prefigured in Margaret O'Callaghan, British high politics and a nationalist Ireland: criminality, land and the law under Forster and Balfour (Cork, 199

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.