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    Application of neural networks for vehicle classifiers: Extreme learning machine approach
    (2019-01-18)
    Jiaramaneepinit, Boonnithi
    ;
    Nuthong, Chaiwat
    Machine learning has been a popular topic in research field for many applications. One of the applications is traffic surveillance system. In many areas, traffic surveillance system is installed in order to gather and estimate important traffic information. Nowadays, there are several systems used for information's extracting, and classifying. One of the well-known approaches is decision tree. It uses a tree-like model that decides consequences outcomes from events. However, in some application, decision tree does not perform well. Another widely used approach is neural network, which has promising performance. It has been developed and become one of the most popular computing systems in research field. The traditional approach in training neural network is backpropagation. However, it has several drawbacks. One of them is the training time. In recent decades, Extreme learning machine (ELM) was proposed for training single hidden layer feed-forward neural network (SLFN) in the extremely fast way. It minimizes training error by utilizing dataset in one-shot calculation. This paper focuses on classifiers in traffic surveillance system. The classification divides into two main tasks. One is vehicle types' classification. Another is vehicle colors' classification. Neural networks trained with ELM are applied to the dataset. The performance are then compared to decision tree based approaches with ensemble methods. The experimental results show that ELM achieves better accuracy than of decision tree based approaches in both tasks.
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    Extended Extreme Learning Machine: A Novel Framework for Neural Network
    (2018-07-02)
    Jiaramaneepinit, Boonnithi
    ;
    Nuthong, Chaiwat
    Machine learning has been a focus research topic of superior tasks in many real-world applications. One of the famous preferred system is neural network. This approach has been invented for decades but becomes popular recently due to its satisfied results in many applications. The success of applying neural network involves model training which conventionally uses backpropagation method. However, it has many drawbacks. In recent decades, Extreme learning machine (ELM) was first proposed for training single-hidden layer feedforward neural network (SLFN). It optimizes training error by utilizing the whole training dataset with a one-shot calculation. However, for the training in datasets with large number of input features or high-dimensional datasets, original ELM encounters many difficulties. One of them is that the original ELM has no learning process from an input layer. This lead to an incomplete representation of data when it is transferred from one layer to another. Another difficulty involves training instability which causes fluctuation in testing accuracy. This is because networks' input weights are randomly generated. To circumvent these difficulties, the imposing architecture, namely Extended Extreme Learning Machine (X-ELM), is proposed. X-ELM uses ELM as an extension part in order to predict the outputs based on ensemble approach. The proposed framework extends the usage of ELM to apply to more complex network structures, such as networks with multiple hidden layers or networks with multiple computing systems. The proposed framework is applied to vehicles characteristic classifications' datasets. The experimental results show that X-ELM achieves better testing accuracy than of ELM in real-world applications.