For hyperspectral data classification, the avoidance of singularity of covariance estimates or excessive near singularity estimation error due to limited training data is a key problem. This study is intended to solve problem via regularized covariance estimators and feature extraction...

In this paper we study a new restriction of the PAC learning framework, in which each label class is handled by an unsupervised learner that aims to fit an appropriate probability distribution to its own data. A hypothesis is derived by choosing, for any unlabeled instance, the label whose...

Based on multiple-case research, this paper develops a model of strategic information systems (IS) investment in small and medium-sized enterprises (SMEs). IS investment is modelled as a function of an SME's strategic context as defined by its strategic focus, i.e. cost reduction versus value...