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自适应共振理论网络 用来进行模式分类联想记忆效果很好,误差率极低,能正确识别-Adaptive Resonance Theory networks used for pattern classification associative memory with good results, the error rate is very low, able to correctly identify
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用ART算法制作的一个神经网络用来分类程序-ART algorithm produced using a neural network for the classification process
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自适应共振神经网络可以实现自动学习与分类具有自适应的功能-adaptive resonance neural network can automatically learning and classification of adaptive function
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基于GPU计算的SVM,VC++源码,包括详细文档说明文件。借用了GPU编程的优势,该代码据作者说比常规的libsvm等算法包的训练速度快13-73倍,预测速度快22-172倍。希望对大家有用-cuSVM is a software package for high-speed (Gaussian-kernelized) Support Vector Machine training and prediction that exploits the massively parallel proc
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algorithm of Fuzzy ART classification
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implementation of ART for classification of letters
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a project for pattern classification by ART neural network (with GUI).
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Signal subspace identification is a crucial first step in many hyperspectral processing algorithms such as target detection, change detection, classification, and unmixing. The identification of this subspace enables a correct dimensionality reductio
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ART1自适应谐振网,已经通过测试,分类性能良好并且准确,此程序已经完成了运行,希望大家好好利用-ART1 adaptive resonance network, has passed the test, the classification performance is good and accurate, this program has completed running, I hope you make good use of
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这是一个关于基于ART的植物种类自动分类的MATLAB程序文件-This is a MATLAB program file on plant-based automatic classification of species ART
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