Handwritten data classification method, model training method and device, equipment and medium
A technology for data classification and model training, applied in character and pattern recognition, instruments, computer components, etc., can solve the problems of low classification accuracy of classifier models and difficulty in adapting to application needs, so as to reduce redundancy and excellent Classification accuracy, the effect of improving accuracy
Active Publication Date: 2018-10-26
SUZHOU UNIV
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However, regardless of the above-mentioned model construction methods, the classification accuracy of the constructed classifier model is still relatively low, and it is difficult to meet the actual application needs
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[0050] The following will clearly and completely describe the technical solutions in the embodiments of the application with reference to the drawings in the embodiments of the application. Apparently, the described embodiments are only some of the embodiments of the application, not all of them. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the scope of protection of this application.
[0051] The embodiment of the present application discloses a handwriting data classification model training method, see figure 1 As shown, the method includes:
[0052] Step S11: Obtain an original training sample set including handwritten data samples and corresponding handwritten category labels.
[0053] In this embodiment, the original training sample set includes multiple categories of handwriting data samples and the category labels corresponding to each handwriting sample. For...
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The invention discloses a handwritten data classification method, a model training method and device, equipment and a medium. The model training method comprises the steps that an original training sample set containing handwritten data samples and corresponding handwritten category tags is acquired; data preprocessing is performed on the original training sample set, so that non-noise data capable of maintaining a manifold structure is selected from the original training sample set, and a processed training sample set is obtained; and the processed training sample set is utilized to train a sparse support vector machine model, and a trained model is obtained. According to the model training method, after the original training sample set is acquired, denoising processing is performed on the original training sample set, only the non-noise data capable of maintaining the manifold structure is reserved, therefore, the redundancy of sample data for model training is lowered, the accuracyof the sample data is improved while the amount of the sample data is reduced, and consequently the model obtained after subsequent training has excellent classification precision.
Description
technical field [0001] The present application relates to the technical field of data classification, in particular to a handwritten data classification method, model training method, device, equipment and media. Background technique [0002] In the existing techniques for classifying handwritten data, the collected raw data is usually directly used to build a classifier model, or the random collection method or K-means clustering algorithm is used to reduce the raw data before building the classifier model. The effect of data volume. However, regardless of the above-mentioned model construction methods, the classification accuracy of the constructed classifier model is still relatively low, and it is difficult to meet the actual application needs. [0003] To sum up, how to improve the accuracy of handwritten data classification results is a problem to be solved at present. Contents of the invention [0004] In view of this, the purpose of this application is to provide...
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IPC IPC(8): G06K9/62
CPCG06F18/214G06F18/241
Inventor 张莉徐志强王邦军张召李凡长
Owner SUZHOU UNIV