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A classifier task based on Neural Turing Machine and particle swarm algorithm
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DOI：10.1016/j.neucom.2018.07.097.png)
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Abstract
En 中文
Deep learning in artificial intelligence looks for a general-purpose computational machine to execute complex algorithms similar to humans' brain. Neural Turing Machine (NTM) as a tool to realize deep learning approach brings together Turing machine that is a general-purpose machine equipped to a long-term memory, and a neural network as a controller. NTM applies simple controllers to execute several simple/complex tasks such as copy, sort, N-gram, etc.; however, complex tasks such as classifications are neglected, and there is no control over improving the weights of NTM, either. This paper presents a framework called PSO-NTM that improves the accuracy of NTM using the LSTM deep neural network as the controller, and implements a complex classification task along with available tasks. Particle Swarm Optimization (PSO) algorithm is also applied in order to control the weights. The classifier task is compared with the basic SVM, KNN, Naive Bayesian, and Decision Tree classification methods on MNIST, ORL, letter recognition, and ionosphere datasets. The accuracy of the proposed classification tasks is 99.73%, 97.9%, 99.02%, and 97.1%, respectively. That means the NTM classification task improved Naive Bayesian 43.57%, Decision Tree 15.6%, and KNN 19.22% on average. In addition, the presented framework improves the available NTM tasks as well. (C) 2019 Elsevier B.V. All rights reserved.
Keywords:
Neural networks
Deep learning
Neural Turing Machine
Particle swarm optimization (PSO)
Classification
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