Deep Convolutional Extreme Learning Machine and Its Application in Handwritten Digit Classification
نویسندگان
چکیده
منابع مشابه
Deep Convolutional Extreme Learning Machine and Its Application in Handwritten Digit Classification
In recent years, some deep learning methods have been developed and applied to image classification applications, such as convolutional neuron network (CNN) and deep belief network (DBN). However they are suffering from some problems like local minima, slow convergence rate, and intensive human intervention. In this paper, we propose a rapid learning method, namely, deep convolutional extreme l...
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In spite of the advances in pattern recognition technology, Handwritten Bangla Character Recognition (HBCR) (such as alpha-numeric and special characters) remains largely unsolved due to the presence of many perplexing characters and excessive cursive in Bangla handwriting. Even the best existing recognizers do not lead to satisfactory performance for practical applications. To improve the perf...
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Recent advances in training deep (multi-layer) architectures have inspired a renaissance in neural network use. For example, deep convolutional networks are becoming the default option for difficult tasks on large datasets, such as image and speech recognition. However, here we show that error rates below 1% on the MNIST handwritten digit benchmark can be replicated with shallow non-convolution...
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Deep networks have inspired a renaissance in neural network use, and are becoming the default option for difficult tasks on large datasets. In this report we show that published deep network results on the MNIST handwritten digit dataset can straightforwardly be replicated (error rates below 1%, without use of any distortions) with shallow ‘Extreme Learning Machine’ (ELM) networks, with a very ...
متن کاملHandwritten Digit Classification
The aim of this project was to evaluate the effectiveness of various types of classifiers in recognizing handwritten digits. It has been shown in pattern recognition that no single classifier performs the best for all pattern classification problems consistently. So the goal of the project was to experiment with different classifiers and combination methods and evaluate their performance in thi...
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ژورنال
عنوان ژورنال: Computational Intelligence and Neuroscience
سال: 2016
ISSN: 1687-5265,1687-5273
DOI: 10.1155/2016/3049632