Description: 该程序实现了对手写数字的识别，识别正确率较高，采用VC环境开发，希望对大家有帮助！-The program achieved the recognition of handwritten digits to identify the correct rate, using VC environment to develop, I hope all of you help! Platform: |
Size: 1693696 |
Author:武玉伟 |
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Description: MATLAB 神经网络用于数字识别源程序，可以很好的识别数字，Digits Recognition有说明-MATLAB neural network for digital identification of the source code, you can identify the number of good, Digits Recognition described Platform: |
Size: 148480 |
Author:何平 |
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Description: 应用matlab程序，实现了对手写数字的识别，准确率非常高。-Matlab application procedures, to achieve the hand-written digit recognition, the accuracy rate is very high. Platform: |
Size: 196608 |
Author:吕晋普 |
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Description: This directory contains the original version of the database
"Optical recognition of handwritten digits" by E.Alpaydin and
C.Kaynak, Department of Computer Engineering, Bogazici University,
80815 Istanbul, Turkey. Platform: |
Size: 437248 |
Author:btttnn |
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Description: CNN Class, ver 0.72.
Change log:
Ver 0.72:
Sample GUI added, demonstrating use of convolutional network for handwriten digits recognition.
Training runs 20 faster.
Ver 0.71:
Bug fix: training was stoped after 1 epoch.
Ver 0.70:
First release.
This project provides matlab class for implementation of convolutional neural networks. -CNN Class, ver 0.72.
Change log:
Ver 0.72:
Sample GUI added, demonstrating use of convolutional network for handwriten digits recognition.
Training runs 20 faster.
Ver 0.71:
Bug fix: training was stoped after 1 epoch.
Ver 0.70:
First release.
This project provides matlab class for implementation of convolutional neural networks. Platform: |
Size: 555008 |
Author:narendra |
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Description: This project provides matlab class for implementation of convolutional neural networks. This networks was created by Yann LeCun and have sucessfully used in many practical applications, such as handwritten digits recognition, face detection, robot navigation and others
This release includes sample of handwritten digits recognition using CNN. Platform: |
Size: 551936 |
Author:ssss |
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Description: 针对10个手写数字的识别问题，设计了一个BP神经网络，使它能够正确识别10个数字。-Against the 10 handwritten numeral recognition problem, a BP neural network is designed so that it can correctly identify the 10 digits. Platform: |
Size: 122880 |
Author:zhangjin |
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Description: 图象处理 模式识别 多种分类方法(最临近匹配分类器、Bayes分类器、线性函数分类、非线性函数分类、神经网络分类)识别0-9数字 手写数字与数字图片,包括设计训练样品库、可以选择多种分类器来识别识别0-9这十个阿拉伯数字，包括临时手写的数字，也包括图片中的数字
-Pattern recognition image processing a variety of classification (the most close to matching classifier, Bayes classifier, a linear function of classification, non-linear function of classification, neural network classification) to identify any number of handwritten digits 0-9 and digital images, including the design of training samples library, you can choose a variety of classifier to identify the 10 Arabic numerals identify the 0-9, including temporary handwritten figures also include the number of pictures Platform: |
Size: 541696 |
Author:yao |
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Description: 采用神经网络实现手写识别的一种方法，建立Bp神经网络，采用快速训练方法，可快速完成一类相关手写字体的模式识别，识别率较高，当字体变化较大识别率降低时，可重新训练具有较强的适应性。实验证实本方法较好实现了手写字符识别，但也存在识别速度较慢，有时训练不收敛等缺点-Handwriting recognition using neural network is a way to establish Bp neural network, using fast training methods, and can be related to the completion of a class of handwriting pattern recognition, recognition rate higher recognition rate when the font changed greatly reduced, they can re- Training has strong adaptability. Experiments confirmed that this method achieved a better handwritten character recognition, but there are also identified slow, and sometimes non-convergence of the training deficiencies Platform: |
Size: 105472 |
Author:caoliang |
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Description: 用Skeletonization剪枝方法精简BP神经网络结构，提高网络泛化能力，对手写数字进行识别。-Skeletonization pruning method using BP neural network architecture to streamline and improve the network generalization ability of the handwritten digits recognition. Platform: |
Size: 175104 |
Author:屠海斌 |
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Description: 介绍了在提取穿越次数特征、粗网格特征以及密度特征提取的基础上应用SVM进行手写体阿拉伯数字识别的方法。-Introduced the extraction across a number of features, coarse grid and density feature extraction on the basis of the application of SVM method for handwritten digits recognition. Platform: |
Size: 24576 |
Author:ping |
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