9 resultados para Information science

em Aberystwyth University Repository - Reino Unido


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Tedd, L.A. (2006).Use of library and information science journals by Master?s students in their dissertations: experiences at the University of Wales Aberystwyth. Aslib Proceedings: New Information Perspectives, 58(6), 570-581.

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Urquhart, C., Light, A., Thomas, R., Barker, A., Yeoman, A., Cooper, J., Armstrong, C., Fenton, R., Lonsdale, R. & Spink, S. (2003). Critical incident technique and explicitation interviewing in studies of information behavior. Library and Information Science Research, 25(1), 63-88. Sponsorship: JISC (for JUSTEIS element)

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Tedd, L.A. (2005). 40 years of library and information studies education in Wales. Education for Information, 23(1/2), 1-8.

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Tedd, L.A. (2003). The What? and How? of education and training for information professionals in a changing world: some experiences from Wales, Slovakia and the Asia-Pacific region. Journal of Information Science, 29(1), 79-86.

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Rowley, J.& Urquhart, C. (2007). Understanding student information behavior in relation to electronic information services: lessons from longitudinal monitoring and evaluation Part 1. Journal of the American Society for Information Science and Technology, 58(8), 1162-1174. Sponsorship: JISC

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Urquhart, C. & Rowley, J. (2007). Understanding student information behavior in relation to electronic information services: lessons from longitudinal monitoring and evaluation Part 2. Journal of the American Society for Information Science and Technology, 58(8), 1188-1197. Sponsorship: JISC

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Bird, A.,& Tedd, L. (2004). Reader development and ICT: an overview of projects in Welsh public libraries. Journal of Librarianship and Information Science, 36(4), 159-174.

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A 5-minute clip from a 1975 video on Bath University Library has been incorporated as slide 21 of this PowerPoint. The video was produced by the author to show library and information science students details of a library with a computer-based cataloguing and circulation system. To make sure that the video clip functions correctly within the presentation, please make sure that you have downloaded both the presentation (.ppt file) and the video (.wmv file) into the same directory. To activate the video move the cursor to the middle of Slide 21 (i.e. the screen that says Bath University) and then click the mouse.

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Feature selection aims to determine a minimal feature subset from a problem domain while retaining a suitably high accuracy in representing the original features. Rough set theory (RST) has been used as such a tool with much success. RST enables the discovery of data dependencies and the reduction of the number of attributes contained in a dataset using the data alone, requiring no additional information. This chapter describes the fundamental ideas behind RST-based approaches and reviews related feature selection methods that build on these ideas. Extensions to the traditional rough set approach are discussed, including recent selection methods based on tolerance rough sets, variable precision rough sets and fuzzy-rough sets. Alternative search mechanisms are also highly important in rough set feature selection. The chapter includes the latest developments in this area, including RST strategies based on hill-climbing, genetic algorithms and ant colony optimization.