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An improvement of extreme learning machine using subclass clustering
(2018-07-02)
Watchareeruetai, Ukrit
;
Jiramaneepinit, Boonnithi
Extreme learning machine (ELM) is an extremely fast learning algorithm proposed for a single-hidden-layer feed-forward neural network (SLFN). ELM projects a set of training instances into a random feature space, and then analytically calculates the weight matrix connecting between the hidden layer and the output layer, leading to a very fast learning speed. This paper proposes an improved version of ELM, named clustering-ELM, that assigns a subclass to each training instances and learns for a weight matrix that projects random features into subclass. In the prediction step, the responses from output nodes of the same class are integrated into one using maximum function. Experimental results conducted on various benchmark datasets reveal a promising performance of the proposed clustering-ELM, compared to the standard ELM.
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Iterative extreme learning machine
(2018-07-02)
Jiramaneepinit, Boonnithi
;
Watchareeruetai, Ukrit
This paper proposes a simple but effective method to improve the generalization performance of extreme learning machine (ELM), which is an extremely fast learning method for a single-hidden-layer feedforward neural network (SLFN). The proposed method adopts an online sequential learning technique to update the output weight matrix of a learned SLFN by using misclassified training samples. As the process of updating these weights could be iteratively performed, the proposed method is named iterative ELM (I-ELM). The proposed I-ELM was evaluated on three datasets, including MNIST, Small NORB, and CIFAR-10, and compared with the standard ELM. Experimental results indicate that by using only a few iterations, the proposed I-ELM could effectively improve the generalization performance of SLFNs.
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An extreme learning machine based pretraining method for multi-layer neural networks
(2018-07-02)
Noinongyao, Pavit
;
Watchareeruetai, Ukrit
One approach in training a deep neural network to perform effectively is to do unsupervised pretraining on each layer, followed by fine-tuning the whole network. A common way is to train an unsupervised model of neural network such as restricted Boltzmann machines or autoencoders and stack them on top of another. Although these unsupervised pretraining approaches yield good performance, relying on back-propagation, due to iterative learning process, they still suffer from a long pretraining time. Extreme learning machine (ELM) is an analytical training approach which is extremely fast and gives a solution with a good generalization performance. In this paper, we apply a new ELM based unsupervised learning, named backward ELM based autoencoder (BELM-AE), to pretrain each layer of a neural network before using a back-propagation based learning algorithm to fine-tune the whole network. Experimental results show that the new pretraining method requires significantly shorter training time and also yields better testing performance on various datasets.

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