登录后查看消息通知
搜索
常见问题
消息
登录
首页
/
数据集
/
Performance of the SVM phenotype recognition model in the stress testa.
Performance of the SVM phenotype recognition model in the stress testa.
收藏
Figshare
2015-12-02 更新
2026-04-29 收录
生物表型识别
SVM支持向量机
数据链接：
https://figshare.com/articles/dataset/_Performance_of_the_SVM_phenotype_recognition_model_in_the_stress_test_a_/327324
数据链接
链接失效反馈
官方服务：
问题咨询
购买咨询
在线客服
NEW
资源简介：
aThe overall recognition accuracy is 93.75%.
应用场景：
创建时间：
2015-12-02
相关数据集
Performances of the optimized SVM models with MOE and IE descriptors on the training set (cross-validation CV) and the external validation set.
分子描述符
SVM支持向量机
Performances of the optimized SVM models with MOE and IE descriptors on the training set (cross-validation CV) and the external validation set.
NIAID Data Ecosystem
7
0
Confusion matrix table of the PCA-SVM test performed on the dataset without PCA and 5-fold CV.
分类模型评估
SVM支持向量机
Confusion matrix table of the PCA-SVM test performed on the dataset without PCA and 5-fold CV.
NIAID Data Ecosystem
4
0
Thresholded maps
SVM支持向量机
权重可视化
Model weight maps and model encoding maps for each SVM pattern .
Figshare
2024-05-22 更新
4
0
SVM classification precision scores.
SVM支持向量机
性能评估
Precision values for SVM classification, provided as a CSV file with comma-separated values. (CSV)
Figshare
2025-09-26 更新
6
0
Performance of SVM based models using amino acid composition as input feature on nine terminus datasets.
蛋白质预测
SVM支持向量机
Sn: Sensitivity; Sp: Specificity; Acc: Accuracy; MCC: Matthew’s Correlation Coefficient.Performance of SVM based models using amino acid composition as input feature on nine terminus datasets.
Figshare
2015-12-03 更新
4
0
© 2023-2026 上海数据发展科技有限责任公司 版权所有
沪ICP备17003045号-15
沪公网安备31010402336585号
热门搜索
社区交流群
科研交流群
商业服务
数据资源
寻源服务
数据采集
标注服务
数据产品
代理销售
数据领域
凭证登记
数据产品
介绍推广
