手写体德文数字的压缩自动编码器和支持向量机识别

2017 1st International Conference on Intelligent Systems and Information Management (ICISIM) Pub Date : 2017-10-01 DOI:10.1109/ICISIM.2017.8122142

R. Kabra

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引用次数: 2

摘要

在机器学习中，数据的表示是非常重要的。更好的表示，分类器将给出更好的结果。压缩自编码器用于学习对输入的微小变化具有鲁棒性的数据表示。本文采用压缩自编码器和支持向量机分类器进行手写体数字识别。CAE+SVM的准确率为96%。

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Contractive autoencoder and SVM for recognition of handwritten Devanagari numerals

Representation of data is very important in case of machine learning. Better the representation, the classifiers will give better results. Contractive autoencoders are used to learn the representation of data which are robust to small changes in the input. This paper uses contractive autoencoder and SVM classifier for handwritten Devanagari numerals recognition. The accuracy obtained using CAE+SVM is 96 %.

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2017 1st International Conference on Intelligent Systems and Information Management (ICISIM)

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