Abstract
This paper adopts 10 machine learning algorithms to present the classification results of handwritten digit recognition on Minist dataset. These algorithms include k-nearest neighbors, support vector machine (SVM), decision trees (DT), random forest (RF), naive bayes, multilayer perception (MLP), logistic regression with neural network, artificial neural network (ANN), back-propagation (BP), convolutional neural network (CNN) and so on. We execute the experiments through matlab2015b and anaconda (python 3.6), and the result (accuracy and run-time) shows that SVM and RF achieve better performance. They has the accuracy of 98.08% and 97% separately, less running-time is taken compared with other methods. All the experiment are executed in CPU environment, without GPU. We also execute CNN algorithm for handwritten digit recognition in GPU (Nvidia GeForce GTX 1060), finally find that this algorithm achieves the best performance and the best classification result, the accuracy is up to 99%.
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This work was financially supported by Wuhan Teaching and Learning Research Programme (2017113).
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Wu, S., Wei, W., Zhang, L. (2018). Comparison of Machine Learning Algorithms for Handwritten Digit Recognition. In: Li, K., Li, W., Chen, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2017. Communications in Computer and Information Science, vol 874. Springer, Singapore. https://doi.org/10.1007/978-981-13-1651-7_47
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DOI: https://doi.org/10.1007/978-981-13-1651-7_47
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