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基于人工神经网络的图像识别方法研究。基于神经网络的人脸检测研究。基于特征融合与神经网络的手写体数字识别技基于遗传神经网络的手写体数字识别研究术研究。基于遗传优化的神经网络的银行票据手写数字识别。一种改进的人工神经网络模型-Based on artificial neural network image recognition method. Neural Network Based Face Detection Research. Based on Feature Fusion and Neur
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通过Fisher线性判别是否为数字，并识别手写数字。-Through the Fisher Linear Discriminant for figures and handwritten numeral recognition.
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模式识别的一个基础程序,手写数字模式识别,提供给大家分享-A basic pattern recognition procedures, handwritten numeral recognition, available to everyone to share
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图像模式识别的各种算法及其编程实现,包括模板匹配分类器、几何分类器和神经网络分类器等，可用于手写数字的识别。-A variety of image pattern recognition algorithm and its programming, including template matching classifier, geometric classifier and neural network classifiers, etc., can be used for handwritte
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MATLAB 神经网络用于数字识别源程序 手写数字识别-MATLAB neural network for digital identification of the source handwritten numeral recognition
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用matlab实现手写数字识别人工神经网络（ArtificialNeuralNetworks，简写为ANNs）也简称为神经网络（NNs）或称作连接模型（ConnectionistModel），它是一种模范动物神经网络行为特征，进行分布式并行信息处理的算法数学模型。-Matlab handwritten numeral recognition
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BP神经网络的手写数字识别 识别数字为0~-BP neural network recognition of handwritten numeral recognition numbers from 0 to 99
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利用小波和RBF神经网络进行手写数字识别-Wavelet and RBF neural network handwritten numeral recognition
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用改进的BP算法实现手写数字识别，并带有GUI界面，是一个很不错的手写数字识别系统。-Improved BP algorithm with handwritten numeral recognition, and with a GUI interface, is a very nice handwritten numeral recognition system.
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采用神经网络方法进行手写数字识别，包括特征提取和识别-
Using the neural network method for handwritten numeral recognition, including feature extraction and recognition
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基于BP神经网络的手写数字识别系统，基于Matlab开发，实现手写输入板功能，特征提取，模型训练，手写识别等功能。详细使用方法在readme说明文档中。-Handwritten numeral recognition system based on BP neural network, developed based on Matlab, handwriting input board, feature extraction, model training, handwriting recogn
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一个基于模式识别的手写数字识别源码，支持学习，以适应不同人手写识别。里面有源码以及测试程序和图片,很适合用来学习-A handwritten numeral recognition based on pattern recognition source code, support learning in order to adapt to different handwriting recognition. There are source code and test procedures an
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采用典型的BP算法实现了基于MNIST的手写数字识别采用输入层、隐含层和输出层的三层结构，实现了BP算法下的神经网络。用7000个样本进行自洽检验，正确率99.79 。-Using typical BP algorithm based on MNIST handwritten numeral recognition using input layer, hidden layer and output layer, three-layer structure, to achieve the neu
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matlab环境下实现对手写体数字的分类识别-Handwritten Arabia digital numeral recognition
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机器学习KNN算法的实现和测试，手写数字识别系统的测试代码-Implementation and test of machine learning KNN algorithm, handwritten numeral recognition system test code implementation and testing of machine learning KNN algorithm, handwritten numeral recognition system test code
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基于Mnist库的手写数字识别的C++源代码，用卷积神经网络实现-Handwritten numeral recognition Mnist library C++ source code, using convolution neural network
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基于Mnist库的手写数字识别的C++源代码，用CNN实现，并且建立了用户界面-Handwritten numeral recognition Mnist library C++ source code, using CNN achieve, and the establishment of a user interface
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手写数字识别系统，能识别0~9，可以进行训练或读取样本文件，选取最符合特征值的前两个显示，可以人工纠错-Handwritten numeral recognition system that can identify 0-9, can be trained or read sample file, the most compatible with the values of the two display features, can be manually Corr
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用VS2012实现手写数字识别的卷积神经网络算法，用mnist库作为输入-Using VS2012 to achieve the handwritten numeral recognition of the convolution neural network algorithm, using the MNIST library as an input
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神经网络的手写 数字识别matlab。包含数据库，可直接运行。-Handwritten numeral recognition matlab neural network.Contains a , can be directly run
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