Performance and efficiency: Recent advances in supervised learning

This paper reviews recent advances in supervised learning with a focus on t wo most important issues: performance and efficiency. Performance addresses the generalization capability of a learning machine on randomly chosen sam ples that are not included in a training set. Efficiency deals with the com plexity of a learning machine in both space and time. As these two issues a re general to various learning machines nod learning approaches, we focus o n a special type of adaptive learning systems with a neural architecture. W e discuss four types of learning approaches. training all individual model; combinations of several well-trained models; combinations of many weak mod els; and evolutionary computation of models. We explore advantages and weak nesses of each approach and their inter relations, and we pose open questio ns for possible future research.