Description: 基于类覆盖算法和粒子群优化的神经网络解决模式分类问题-algorithm based on the type coverage and PSO the neural network model to solve classification problems Platform: |
Size: 1370112 |
Author:老农 |
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Description: K-MEANS聚类算法，以及PSO和QPSO算法改进K-MEANS算法，breastcancer数据验证了该分类模型的有效性-K-MEANS clustering algorithm, and the PSO algorithm and QPSO to improve K-MEANS algorithm, breastcancer data verified the validity of the classification model Platform: |
Size: 43008 |
Author:李慧 |
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Description: svm多分类器，包括多分类和GA算法和PSO算法优化的SVM-svm multi-classifier, including the multi-classification and GA algorithm and PSO algorithm for optimization of SVM Platform: |
Size: 494592 |
Author:何同学 |
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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: |
Size: 565248 |
Author:alice |
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Description: 用PSO优化LSSVM的参数，得到最优参数，使得分类更加准确。-Using the PSO the LSSVM the parameter, the optimal parameters, more accurate classification.
Platform: |
Size: 3072 |
Author:许莎 |
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Description: 一种基于小波支持向量机和粒子群算法的多故障诊断技术。-Multi-fault classification based on wavelet SVM with PSO algorithm
to analyze vibration signals from rolling element bearings Platform: |
Size: 1496064 |
Author:袁可红 |
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Description: 自己修改的一个源程序，用粒子群算法优化RBF神经网络的中心值，宽度，权值，可以直接运行~喜欢分类、预测的朋友们，可以下载！-Modify a source particle swarm algorithm to optimize the center of the RBF neural network value, width, weight, and can be run directly to like classification, prediction friends, you can download! Platform: |
Size: 3072 |
Author:张琪 |
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Description: 自己修改的一个源程序，用粒子群算法优化RBF神经网络的中心值，宽度，权值，喜欢分类、预测的朋友们可以下载！-Modify a source particle swarm algorithm to optimize the center of the RBF neural network value, width, weight, and can be run directly to like classification, prediction friends, you can download! Platform: |
Size: 3072 |
Author:张琪 |
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Description: 实现了 SVM 在医学图像分类中的应用，通过医学图像的实验结果验证了该方法的有效性。
对 SVM 算法进行了深入研究，结合量子行为的粒子群算法的优点，提出了一种量子行为的粒子群算法和 SVM 结合的混合分类模型，应用于医学图像的分类中。
深入研究了 K-means 算法，结合了无监督聚类和监督分类方法的优点，提出了一种 K-means 和 SVM 结合的混合分类模型，并应用于医学图像的分类中。
利用粒子群和量子行为的粒子群算法对 K-means 算法进行了改进，并将改进的K-means 算法与 SVM 算法结合形成全自动分类模型，并且把它应用于医学图像的分类中。
-SVM in the classification of medical image applications, medical images through the experimental results verify the effectiveness of the method. SVM algorithm conducted in-depth research, combined with the quantum behavior of the advantages of particle swarm optimization, PSO and SVM combined behavior of a quantum mixed classification model, applied to medical image classification. In-depth study of the K-means algorithm combines the advantages of unsupervised clustering and supervised classification method, propose a hybrid combination of a K-means and SVM classification model, and applied to the classification of medical images. Improved particle swarm algorithm using particle swarm and quantum behavior of the K-means algorithm, and improved K-means algorithm and SVM algorithm combined with the formation of a fully automatic classification model, and apply it to medical image classification. Platform: |
Size: 1626112 |
Author:招伟杰 |
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Description: pso优化lssm的matlab程序，用于文件文本等的分类或者研究工作-Pso- LSSM matlab used in file, such as classification or research work Platform: |
Size: 3072 |
Author:cao cheng |
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Search - PSO Classification