Description: 一种基于蚁群聚类的径向基神经网络
提出了一种基于蚁群聚类算法的径向基神经网络. 利用蚁群算法的并行寻优特征和挥发系
数方法的自适应更改信息量的能力,并以球面聚类的方式确定了径向基神经网络中基函数的位置,
同时通过比较隐层神经元的相似性、合并相似性较为接近的2 个神经元来约简隐含层的神经元,以
达到简化径向基神经网络结构的目的. 实验比较了几种不同聚类算法的径向基神经网络,结果表
明,所提神经网络的整体训练时间至少可缩短40 ％ ,学习的准确率可提高1 ％以上,而且网络结构
更加精简.-An Ant clustering RBFNN presents a clustering algorithm based on the ant colony RBFN Liaison. Ant use the parallel algorithm optimization features and volatile coefficient method of adaptive changes in the volume of information, spherical cluster and the method of RBF neural network-based functions of the position, By comparing the same time hidden layer neurons of similarity, Merger similarity is closer to the two neurons to Jane about the hidden layer neurons, to achieve simplification RBF neural network structure purposes. Experimental comparison of several different clustering algorithm RBF neural network, The results show that the neural network's overall training time can be shortened at least 40%. Learning the accuracy rate can be increased by 1%, and the network is more streaml Platform: |
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Author:zhxj |
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Description: kd树的最快实现算法，经过了数次优化后，即使处理数十万个点，也仅仅需要几秒钟，内含使用说明。-kd tree algorithm to achieve the fastest, after a number of optimization, even hundreds of thousands of points. I only need a few seconds, intron use. Platform: |
Size: 207872 |
Author:胖丫 |
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Description: 是一个基于Matlab软件的模糊C-means聚类算法原代码-Matlab is a software based on the fuzzy C-means clustering algorithm source code Platform: |
Size: 2048 |
Author:杨明 |
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Description: 提出了一种新颖的处理图像视觉聚类问题的方法。与以往关注图像局部特征和局部连续性的方法不同，本文中的方法能够提取关于图像的全局印象。为此，我们将图像分割问题转化为图划分问题并提出了划分中的一种全局判别准则——Ncut (Normalized Cut)。Ncut不仅能够衡量不同聚类之间的相异程度，还能够衡量各聚类内部的相似程度。为求解Ncut 的最优化问题，提出了一种基于广义特征值问题的高效算法，并将此算法应用于静态图像分割，取得了良好的效果。-proposed a novel image processing visual clustering problem. Concerned with the past, image characteristics and continuity of the partial difference in the way this method can extract images on the overall impression. To this end, We will image segmentation into the map division and the division of a global criterion-- Ncut ( Normalized Cut). Ncut different not only to measure the difference between the cluster level, but also to measure the internal cluster degree of similarity. Ncut to solve the optimization problem, based on a generalized eigenvalue problem of efficient algorithms, this algorithm will be applied to the static image segmentation, and achieved good results. Platform: |
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Author:戚颖杰 |
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Description: 经网络提出了一种基于蚁群聚类算法的径向基神经网络. 利用蚁群算法的并行寻优特征和挥发系数方法的自适应更改信息量的能力,并以球面聚类的方式确定了径向基神经网络中基函数的位置, 同时通过比较隐层神经元的相似性、合并相似性较为接近的2 个神经元来约简隐含层的神经元,以达到简化径向-via the Internet presents a clustering algorithm based on the ant colony RBFNN. Ant use the parallel algorithm optimization Volatile coefficient characteristics and the method of adaptive changes in the volume of information, spherical cluster and the method of RBF neural network-based functions of the position, By comparing the same time hidden layer neurons of similarity, Merger similarity is closer to the two neurons to Jane about the hidden layer neurons to achieve simplification Radial Platform: |
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Author:何泼 |
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Description: 粒子群优化算法(PSO)是一种进化计算技术(evolutionary computation)，有Eberhar博士和kennedy博士发明。源于对鸟群捕食的行为研究 ，PSO同遗传算法类似，是一种基于叠代的优化工具。
-Particle Swarm Optimization (PSO) is an evolutionary computation technique (evolutionary computation), and has Eberhar Dr. Dr. kennedy invention. Stems from the behavior of predatory birds, PSO with genetic algorithm is similar to an iterative optimization-based tools. Platform: |
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Author:叶开 |
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Description: 统计模式识别工具箱（Statistical Pattern Recognition Toolbox）包含：
1，Analysis of linear discriminant function
2，Feature extraction: Linear Discriminant Analysis
3，Probability distribution estimation and clustering
4，Support Vector and other Kernel Machines-
This section should give the reader a quick overview of the methods implemented in
STPRtool.
• Analysis of linear discriminant function: Perceptron algorithm and multiclass
modification. Kozinec’s algorithm. Fisher Linear Discriminant. A collection
of known algorithms solving the Generalized Anderson’s Task.
• Feature extraction: Linear Discriminant Analysis. Principal Component Analysis
(PCA). Kernel PCA. Greedy Kernel PCA. Generalized Discriminant Analysis.
• Probability distribution estimation and clustering: Gaussian Mixture
Models. Expectation-Maximization algorithm. Minimax probability estimation.
K-means clustering.
• Support Vector and other Kernel Machines: Sequential Minimal Optimizer
(SMO). Matlab Optimization toolbox based algorithms. Interface to the
SVMlight software. Decomposition approaches to train the Multi-class SVM classifiers.
Multi-class BSVM formulation trained by Kozinec’s algorithm, Mitchell-
Demyanov-Molozenov algorithm Platform: |
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Author:查日东 |
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Description: CA算法可以将数量型属性划分成若干个优化的区间，它综合了分层聚类于划分聚类的优点，对于给定的不同的初始类个数，CA算法能随着迭代过程的不断进展改变类的数目，一些竞争力差的类即类的基数小于给定阙值的类将在迭代过程中不断消失，最终得到能够有效体现数据实际分布情况的优化聚类个数。-CA algorithm for quantitative attributes can be divided into a number of the interval optimization, hierarchical clustering which combines the advantages of clustering in the division, for a given number of different types of initial, CA algorithm as the iterative process the continuous progress of change in the number of categories, some categories of poor competitiveness of the base type that is less than the value of a given category Que iterative process will continue to disappear and eventually be able to effectively reflect the actual distribution of data to optimize the number of clustering. Platform: |
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Author:王锋 |
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Description: 模糊C-均值算法容易收敛于局部极小点，为了克服该缺点，将遗传算法应用于模糊C-均值算法(FCM)的优化计算中，由遗传算法得到初始聚类中心，再使用标准的模糊C-均值聚类算法得到最优分类结果。-Fuzzy C-means algorithm converges to local minimum points easily, in order to overcome the shortcomings of genetic algorithm is applied to fuzzy C-means algorithm (FCM) of the optimization calculations, by the genetic algorithm is the initial cluster centers, and then use the standard Fuzzy C-Means clustering algorithm to obtain the optimal classification result. Platform: |
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Author:zsy |
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Description: 这些文献包括，分类，聚类，以及多目标优化，可以帮助大家进一步学习和接触此方面的知识，进而进一步发展-These documents include classification, clustering, and multi-objective optimization, which can help us to further study and access to the further development of this knowledge. Platform: |
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Author:武翠霞 |
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Description: A distributed PSOSVM hybrid system with feature selection and parameter optimization
-Abstract
This study proposed a novel PSO–SVM model that hybridized the particle swarm optimization (PSO) and support vector machines (SVM) to
improve the classification accuracy with a small and appropriate feature subset. This optimization mechanism combined the discrete PSO with the
continuous-valued PSO to simultaneously optimize the input feature subset selection and the SVM kernel parameter setting. The hybrid PSO–SVM
data mining system was implemented via a distributed architecture using the web service technology to reduce the computational time. In a
heterogeneous computing environment, the PSO optimization was performed on the application server and the SVM model was trained on the
client (agent) computer. The experimental results showed the proposed approach can correctly select the discriminating input features and also
achieve high classification accuracy.
# 2007 Elsevier B.V. All rights reserved. Platform: |
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Author:alice |
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Description: Cluster analysis one of the several important tools in
modern data analysis, and the clustering can be regarded
as an optimization problem. The underlying assumption is
that there are natural tendencies of cluster or group
structure in the data and the goal is to be able to uncover
this structure. In general, traditional clustering
algorithms are suitable to implement clustering only if the
feature differences of data are large.-Cluster analysis is one of the several important tools in
modern data analysis, and the clustering can be regarded
as an optimization problem. The underlying assumption is
that there are natural tendencies of cluster or group
structure in the data and the goal is to be able to uncover
this structure. In general, traditional clustering
algorithms are suitable to implement clustering only if the
feature differences of data are large. Platform: |
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Author:rishi |
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Search - clustering optimization