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Deep extreme learning machines: supervised autoencoding architecture for classification
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DOI：10.1016/j.neucom.2015.03.110.png)
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We present a method for synthesising deep neural networks using Extreme Learning Machines (ELMS) as a stack of supervised autoencoders. We test the method using standard benchmark datasets for multiclass image classification (MNIST, CIFAR-10 and Google Streetview House Numbers (SVHN)), and show that the classification error rate can progressively improve with the inclusion of additional autoencoding ELM modules in a stack. Moreover, we found that the method can correctly classify up to 99.19% of MNIST test images, which surpasses the best error rates reported for standard 3-layer ELMs or previous deep ELM approaches when applied to MNIST. The approach simultaneously offers a significantly faster training algorithm to achieve its best performance (in the order of 5 min on a four-core CPU for MNIST) relative to a single ELM with the same total number of hidden units as the deep ELM, hence offering the best of both worlds: lower error rates and fast implementation. (C) 2015 Elsevier B.V. All rights reserved.
Keyword:
Extreme learning machine
Supervised learning
Autoencoder
Classifier
MNIST
Deep neural network
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6.5
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2.5W
被引数:
6.5W
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