Abstract. We establish a generic theoretical tool to construct probabilistic bounds for algorithms where the output is a subset of objects from an initial pool of candidates (or mo...
Conventional classification learning allows a classifier to make a one shot decision in order to identify the correct label. However, in many practical applications, the problem ...
This paper is concerned with personalisation of user agents by symbolic, on-line machine learning techniques. The application of these ideas to an infotainment agent is discussed ...
Joshua J. Cole, Matt J. Gray, John W. Lloyd, Kee S...
Logic programming provides a uniform framework in which all aspects of explanation-based generalization and learning may be defined and carried out, but first-order Horn logic i...
In this paper we investigate the regularization property of Kernel Principal Component Analysis (KPCA), by studying its application as a preprocessing step to supervised learning ...