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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
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Size: 42789 |
Author: 李慧 |
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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
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Size: 43008 |
Author: 李慧 |
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Description: Cancerdectionusing new algorithm
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Size: 5120 |
Author: bala |
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Description: This function matlab provides, bio-medical function.
files include:
allCode.m
breastcancer.mat
flicker.mat
medfiles.mat
myfit.m
plotcluster.m
sulfate.mat-This is function matlab provides, bio-medical function.
files include:
allCode.m
breastcancer.mat
flicker.mat
medfiles.mat
myfit.m
plotcluster.m
sulfate.mat
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Size: 6144 |
Author: socosmo |
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Description: Code for Classification Accuracy of KNN, C4.5 and SVM algo in R
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Size: 19456 |
Author: yamini |
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Description: breast Cancer Classification
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Size: 20274176 |
Author: Devillers
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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()
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Size: 26624 |
Author: rrchitejwurl
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Description: Java实现机器学习经典分类算法，代码中实现了决策树、贝叶斯和KNN三个分类算法(Java implements the classic classification algorithm for machine learning. The code implements three classification algorithms: decision tree, Bayes and KNN)
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Size: 87040 |
Author: sasworld |
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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()
Platform: |
Size: 26624 |
Author: Abelit |
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Description: 选择“BreastCancer”数据集，使用支持向量机(SVM)对其进行分类。作为对比，第一次对特征集直接进行支持向量机分类，第二次对特征集进行主成分分析法的特征提取后，再对特征提取后的特征集进行支持向量机分类。并且对比和分析了两次分类的结果。(The BreastCancer data set is selected and classified by Support Vector Machine (SVM). For comparison, the first time the feature set is classified directly by support vector machine, the second time the feature set is extracted by principal component analysis, and then the feature set is classified by support vector machine. The results of the two classifications are compared and analyzed.)
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Size: 78848 |
Author: yty1018 |
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Description: 针对“BreastCancer”数据集，作为对比，第一次对特征集直接进行SVM分类，第二次使用粒子群算法进行特征选择后再进行SVM分类。并且对比和分析了两次分类的结果。(For "BreastCancer" data set, as a comparison, the first time the feature set is directly classified by SVM, and the second time the feature set is selected by particle swarm optimization before SVM classification. The results of the two classifications are compared and analyzed.)
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Size: 281600 |
Author: yty1018 |
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