期刊
JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY
卷 73, 期 1, 页码 73-81出版社
SPRINGER
DOI: 10.1007/s11265-013-0730-x
关键词
Extreme learning machine; Differential evolution; Image analysis; Face recognition
Extreme learning machine (ELM) and evolutionary ELM (E-ELM) were proposed as a new class of learning algorithm for single-hidden layer feedforward neural network (SLFN). In order to achieve good generalization performance, E-ELM calculates the error on a subset of testing data for parameter optimization. Since E-ELMemploys extra data for validation to avoid the overfitting problem, more samples are needed for model training. In this paper, the cross-validation strategy is proposed to be embedded into the training phase so as to solve the overtraining problem. Based on this new learning structure, two extensions of E-ELM are introduced. Experimental results demonstrate that the proposed algorithms are efficient for image analysis.
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