搜索资源列表
GEBRIE
- DOCUMENTS ON BREAST CANCER
biomasscaulature
- 林木生物量方程和林分树高胸径方程及其参数初值(The equation of tree biomass and the high breast diameter equation of the forest tree and its initial parameters)
chapter28
- 决策树分类器在乳腺癌诊断中的应用研究(2012b版本)(Application of decision tree classifier in breast cancer diagnosis (2012b version))
LVQ神经网络的分类——乳腺肿瘤诊断
- LVQ神经网络的分类——乳腺肿瘤诊断,matlab(Classification of LVQ neural network -- diagnosis of breast tumor)
ML
- 关于机器学习分类算法应用的一个例子,主要是根据给定数据对良/恶性乳腺癌肿瘤预测的实际应用,如何使用分类算法(An example of the application of the machine learning classification algorithm is mainly based on the practical application of the given data to the prediction of benign / malignant breast cancer,
模式识别代码
- 基于matlab的Iris、乳腺癌数据集的模式识别分类算法,含有 遗传算法+SVM、isodata、感知器算法、LMSE、神经网络等算法的实现代码,用于聚类效果良好,是模式识别大作业的参考资料(The pattern recognition classification algorithm based on MATLAB for Iris and breast cancer data sets contains the implementation code of genetic algorit
PCA
- 对一个数据表格的特征进行提取,从而得出哪些特征对乳腺癌肿瘤的影响效果最大(The characteristics of a data table are extracted to find out which characteristics have the greatest impact on breast cancer.)
Breast_cancer_detection
- 关于威斯康星乳腺癌数据的分类程序,直接可用。需要打标。(The classification procedure for breast cancer data in Wisconsin is directly available. You need to make a bid.)
apriori-medical
- 借助乳腺癌患者的病理信息,挖掘患者症状与中医症状之间的关联关系;为治疗提供依据,挖掘潜在的病症因素。(With the help of pathological information of breast cancer patients, the relationship between patient symptoms and TCM symptoms can be excavated.)
random forest
- 此源程序包含随机森林工具箱,以及将随机森林算法用于乳腺肿瘤数据的分类预测(This source includes random forest toolbox, and random forest algorithm is used to classify and predict breast tumor data.)
uci-breast-cancer-master
- 机器学习中的随机森林算法,用于空气质量预测(Random forest algorithm in machine learning for air quality prediction)
classifier_D
- 使用SVM分类器来预测乳腺癌病人的预后(特征选择;分类器构建),评价模型时使用无被交叉验证,性能评价指标包括准确率,AUC,灵敏度,特异度。学会最基本的机器学习方法。可查看分发给大家的代码,以后遇到类似的问题,可用相似的思路和代码。(The SVM classifier was used to predict the prognosis of breast cancer patients (feature selection; classifier construction), and the