Computational Intelligence and Feature Selection : Rough and Fuzzy Approaches

Computational Intelligence and Feature Selection provides a high level audience with both the background and fundamental ideas behind feature selection with an emphasis on those techniques based on rough and fuzzy sets, including their hybridizations. It introduces set theory, fuzzy set theory, rough set theory, and fuzzy-rough set theory, and illustrates the power and efficacy of the feature selections described through the use of real-world applications and worked examples. Program files implementing major algorithms covered, together with the necessary instructions and datasets, are available on the Web.

Teaching from Examples in Assembly and Manipulation of Snack Food Ingredients by Robot

T. G. Williams, J.J. Rowland, and Lee M.H., Teaching from Examples in Assembly and Manipulation of Snack Food Ingredients by Robot, Proc. IEEE/RSJ Int. Conf. on Robots and Systems (IROS 2001), Nov., 2001, pp2300-2305.

Assessing training and professional development needs of library staff. Report for National Library of Health

Urquhart, C., Spink, S. & Thomas, R., Assessing training and professional development needs of library staff. Report for National Library of Health. (2005). Aberystwyth: Department of Information Studies, University of Wales Aberystwyth Sponsorship: National Library for Health (NHS Information Authority)