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3D-handwritten-recognition SVM classifier nuclear-parameter selection method and purpose thereof

A technology of handwriting recognition and kernel parameters, which is applied in the field of 3D handwriting recognition, can solve problems such as limited application, unstable algorithm convergence, and insufficient convergence speed of the firefly algorithm, so as to make up for the decline in accuracy, increase the convergence speed, and improve the recognition rate.

Inactive Publication Date: 2014-07-23
ZHEJIANG UNIV
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AI Technical Summary

Problems solved by technology

However, the convergence speed of the firefly algorithm is not fast enough, and the convergence instability will appear in the later stage of the algorithm. These shortcomings limit the application of the firefly algorithm in 3D handwriting recognition.

Method used

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  • 3D-handwritten-recognition SVM classifier nuclear-parameter selection method and purpose thereof
  • 3D-handwritten-recognition SVM classifier nuclear-parameter selection method and purpose thereof
  • 3D-handwritten-recognition SVM classifier nuclear-parameter selection method and purpose thereof

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Embodiment Construction

[0020] The present invention mainly relates to the algorithm improvement of SVM kernel parameter selection, such as figure 1 As shown, it shows that adding the brightness factor when updating the position will affect the size of the moving step; combined with figure 1 The specific process of selecting SVM kernel parameters is as follows:

[0021] In the basic firefly algorithm, firstly, n fireflies are randomly distributed in the solution space, each firefly has its own brightness initial value, and their brightness is related to the function value of the current position, the better the position, the The higher the brightness. Each firefly has a line of sight (also known as a dynamic decision-making domain), within which it looks for fireflies that are brighter than itself, and forms a neighbor set, and then selects the firefly with the highest relative brightness through the roulette probability method and move towards it. After moving, update its own brightness, position...

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Abstract

The invention discloses a 3D-handwritten-recognition SVM classifier nuclear-parameter selection method which improves a position update formula of glowworms in a GSO (Glowworm Swarm Optimization) algorithm and introduces lightness features in an individual movement process so that accuracy and convergence of the algorithm are improved significantly and thus an optimal SVM nuclear function parameter is selected and a classifier excellent in performance is constructed. Through use of the method, a better 3D-handwritten-recognition system can be constructed and the identification rate of the 3D-handwritten-recognition system is improved effectively.

Description

technical field [0001] The invention belongs to the technical field of 3D handwriting recognition, and in particular relates to the problem of SVM classifier optimization in the 3D handwriting recognition technology. Background technique [0002] Handwriting recognition technology is a popular technology gradually developed under the trend of human-computer interaction technology. Compared with the traditional plane handwriting recognition, 3D handwriting recognition is an emerging handwriting recognition technology, which can provide users with a more natural and efficient human-computer interaction experience. In recent years, it has gradually become a research hotspot of handwriting recognition technology. The development trend of handwriting recognition in the future. [0003] For a long time, the kernel function parameter optimization problem of the SVM classifier is a key technology in the 3D handwriting recognition process, and the performance of the kernel function ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/66G06N3/00
Inventor 沈海斌杨海
Owner ZHEJIANG UNIV