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Performance and efficiency: Recent advances in supervised learning
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DOI：10.1109/5.784228.png)
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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 nod learning approaches, we focus on a special type of adaptive learning systems with a neural architecture. We discuss four types of learning approaches. training all 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 inter relations, and we pose open questions for possible future research.
Keyword:
evolutionary computation
hybrid models
neural networks
supervised learning
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25.9
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