Description: 神经网络中的无监督学习中的SOM学习算法，并在MFC中以主观方式显示学习过程。-Neural network unsupervised learning of SOM learning algorithm, and in MFC in order to show the subjective learning process. Platform: |
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Author:三方 |
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Description: 人工神经网络理论，主要讲述在神经计算中常用的各种无指导算法，比如Hebbian学习-Artificial neural network theory, mainly in the neural computation about the various commonly used unsupervised algorithms, such as Hebbian learning Platform: |
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Author:吴奇 |
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Description: 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，获胜节点及其周围节点的连接权矢量向输入模式的方向作相应的修正。当输入模式类别发生变化时，二维平面上的获胜节点也从原来节点转移到其它节点。这样，网络通过自组织方式用大量训练样本数据来调整网络的连接权值，最后使得网络输出层特征图能够反映样本数据的分布情况。根据SOM网络的输出状况，不仅能判断输入模式所属的类别，使输出节点代表某类模式，而且能够得到整个数据区域的分布情况，即从样本数据得到所有数据的分布特征。 -SOM neural network (self-organizing feature map neural network) is an unsupervised neural network. Network topology is an input layer and an output layer. Input layer nodes is the input dimension of the sample, each node represents a component input samples. Output layer nodes are arranged in two-dimensional array structure. X in the input layer and output layer each node of each neuron node Y by a weight (the weight vector as W) is connected, so that each output layer corresponds to a connection node of the right vector.
Self-organizing feature maps of the basic principle is, when each category of inputs into the model, its output layer one node get the maximum boost and win, Huoshengjiedian around Yixiejiedian Yin Zuo Yong Ye Shoudaojiaotai lateral stimulation. Then a learning network operation, the winner node and surrounding nodes in the right direction vector to the input mode to make consequential amendments. When the input mode type changes, the two-dimensional plane of the wi Platform: |
Size: 47104 |
Author:leidan |
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Description: SOM 是神经网络中很重要用C语言实现的算法。对于学习神经网络的人来说，可以学习学习。-SOM is an unsupervised neural network algorithm. The algorithm in the neural network has a significant impact. Learning neural network for people who are very important. I hope this code can give you help. Platform: |
Size: 64512 |
Author:楚天 |
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Description: 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 layer-recurrent
* Unsupervised networks including self-organizing maps and competitive layers
* Preprocessing and postprocessing for improving the efficiency of network training and assessing network performance
* Modular network representation for managing and visualizing networks of arbitrary size
* Routines for improving generalization to prevent overfitting
* Simulink® blocks for building and evaluating neural networks, and advanced blocks for control systems applications-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 layer-recurrent
* Unsupervised networks including self-organizing maps and competitive layers
* Preprocessing and postprocessing for improving the efficiency of network training and assessing network performance
* Modular network representation for managing and visualizing networks of arbitrary size
* Routines for improving generalization to prevent overfitting
* Simulink® blocks for building and evaluating neural networks, and advanced blocks for control systems applications Platform: |
Size: 252928 |
Author:bacha |
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Description: 无监督神经网络利用matlab能够有效的实现图像化，还能得到最有结果-Unsupervised neural network using matlab can effectively realize the image of, but also get the most results Platform: |
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Author:suntao |
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Description: A self-organizing map (SOM) or self-organizing feature map (SOFM) is a type of artificial neural network (ANN) that is trained using unsupervised learning to produce a low-dimensional (typically two-dimensional) Platform: |
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Author:praveenpaul |
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Description: 包括无监督和监督的机器学习技术
• K-means and other clustering tools
• Neural Networks
• Decision trees and ensemble learning
• Naï ve Bayes Classification
• Linear, logistic and nonlinear regression-Highlights include unsupervised and supervised machine learning techniques including:
• K-means and other clustering tools
• Neural Networks
• Decision trees and ensemble learning
• Naï ve Bayes Classification
• Linear, logistic and nonlinear regression Platform: |
Size: 903168 |
Author:Kevin |
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Description: The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in
which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in
practice. More recently, neural network techniques and methods imported statistical learning theory have been receiving
increasing attention. The design of a recognition system requires careful attention to the following issues: definition of pattern classes,
sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection
of training and test samples, and performance evaluation.-The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in
which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in
practice. More recently, neural network techniques and methods imported statistical learning theory have been receiving
increasing attention. The design of a recognition system requires careful attention to the following issues: definition of pattern classes,
sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection
of training and test samples, and performance evaluation. Platform: |
Size: 1364992 |
Author:jincy |
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Description: 自组织特征映射神经网络是一类无导师学习的神经网络模型可用于聚类分析-Self-organizing neural network is a kind of unsupervised learning neural network model can be used for cluster analysis Platform: |
Size: 820224 |
Author:sunshine |
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Search - neural unsupervised