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Performance and efficiency: recent advances in supervised learning

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2 Author(s)
Sheng Ma ; IBM Thomas J. Watson Res. Center, Hawthorne, NY, USA ; Chuanyi Ji

This paper reviews recent advances in supervised learning with a focus on two most important issues: performance and efficiency. Performance addresses the generalization capability of a learning machine on randomly chosen samples that are not included in a training set. Efficiency deals with the complexity of a learning machine in both space and time. As these two issues are general to various learning machines and learning approaches, we focus on a special type of adaptive learning systems with a neural architecture. We discuss four types of learning approaches: training an individual model; combinations of several well-trained models; combinations of many weak models; and evolutionary computation of models. We explore advantages and weaknesses of each approach and their interrelations, and we pose open questions for possible future research

Published in:

Proceedings of the IEEE  (Volume:87 ,  Issue: 9 )