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Basic library that implements Kohonen s SOM and its learning. Lanuage: C# (.Net 3.5 Framework)
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Thesis from the Charles University (Prague) about Kononen s Self-organizing maps. In English.
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自组织特征映射网络matlab 的仿真 供大家分享~ -Self-organizing feature maps of matlab simulation for network share
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It s toolbox in determining the crime pattern analysis using SOM (Self-Organizing Maps). It s using SOM clusters and K-means clusters.
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this algorithm in matla help to apply som self organizing maps-this algorithm in matla help to apply som self organizing maps
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This program is a simple demonstration of a Kohonen self-organizing neural network. The program merely maps itself to a set of coordinates ranging from -0.5 to 0.5 on both the x and y-axis.
The program layout is very simple - the Run button will
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An Introduction to Kohonen Self Organizing Maps
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SOM神经网络（自组织特征映射神经网络）是一种无导师神经网路。网络的拓扑结构是由一个输入层与一个输出层构成。输入层的节点数即为输入样本的维数，其中每一节点代表输入样本中的一个分量。输出层节点排列结构是二维阵列。输入层X中的每个节点均与输出层Y每个神经元节点通过一权值(权矢量为W)相连接，这样每个输出层节点均对应于一个连接权矢量。
自组织特征映射的基本原理是，当某类模式输入时，其输出层某一节点得到最大刺激而获胜，获胜节点周围的一些节点因侧向作用也受到较大刺激。这时网络进行一次学习操作，获胜节点
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Self Organizing Maps In Natural Language Processing
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自组织特征映射的一种基本算法，供初学者使用-Self-organizing maps as a basic algorithm for beginners
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神经网络原理 Simon Haykin
基本涵盖了神经网络的许多基础部分和重要方面。像Back Propagation， Radial-Basis Function，Self-Organizing Maps，以及single neuron中的Hebbian Learning, Competitive Learning和LMS Learning。 -Neural network theory Simon Haykin covering the basic part of the neural
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神经网络原理 Simon Haykin 英文版
基本涵盖了神经网络的许多基础部分和重要方面。像Back Propagation， Radial-Basis Function，Self-Organizing Maps，以及single neuron中的Hebbian Learning, Competitive Learning和LMS Learning。 -Neural network theory Simon Haykin in English covering the neural net
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A new design of a music note recognition system based on
time delay neural networks, self organizing maps, and linear
vector quantization is proposed in this paper, and the general
designs of two possible applications based on this system are
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Self organizing maps for Matlab CUDA
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Key Features
* Neural network design, training, and simulation
* Pattern recognition, clustering, and data-fitting tools
* Supervised networks including feedforward, radial basis, LVQ, time delay, nonlinear autoregressive (NARX), and laye
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神经网络SOM网络模型 运用自组织模型给数据分类-Use self-organizing maps (SOM) to reduce the data into clusters
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自组织特征映射模型(Self-Organizing feature Map)，认为一个神经网络接受外界输入模式时，将会分为不同的区域，各区域对输入模式具有不同的响应特征，同时这一过程是自动完成的。各神经元的连接权值具有一定的分布。最邻近的神经元互相刺激，而较远的神经元则相互抑制，更远一些的则具有较弱的刺激作用。自组织特征映射法是一种无教师的聚类方法。-Self-organizing maps model (Self-Organizing feature Map), that a neural n
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自组织映射神经网络java源代码 myeclipse工程文件可直接导入使用-self-organizing maps nerual network java source code
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Sample code for learning Competitive Learning and Self Organizing Maps using Matlab.
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SOM神经网络，采用自组织映射神经网络，myeclipse可导入工程直接使用(Self-organizing maps nerual network java source code.)
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