Case base adaptation using solution-space metrics

In this paper we propose a generalisation of the k-nearest neighbour (k-NN) retrieval method based on an error function using distance metrics in the solution and problem space. It is an interpolative method which is proposed to be effective for sparse case bases. The method applies equally to nominal, continuous and mixed domains, and does not depend upon an embedding n-dimensional space. In continuous Euclidean problem domains, the method is shown to be a generalisation of the Shepard's Interpolation method. We term the retrieval algorithm the Generalised Shepard Nearest Neighbour (GSNN) method. A novel aspect of GSNN is that it provides a general method for interpolation over nominal solution domains. The performance of the retrieval method is examined with reference to the Iris classification problem,and to a simulated sparse nominal value test problem. The introducion of a solution-space metric is shown to out-perform conventional nearest neighbours methods on sparse case bases.

Developing global trade union "space"

International unions and international solidarity strategies have been changing partly as a response to changes in the global economy. Global union federations have played an important role in expanding communication and developing alliances with other social movements. One of the issues facing trade unions wanting to be effective at an international level is to what extent national concerns can inform and even be transcended by international perspectives. Proposals for studying the global labor force "horizontally", that is, according to different categories and forms of work rather than on a nation-by-nation basis, demonstrates a recognition of global- national dimensions (Harrod and O'Brien 2002:49). This paper will discuss the development of a global trade union "space", which enables national and global unions to work together effectively, using a series of case studies drawn from the experience of global and European public service unions.