搜索资源列表
Hopfield&Application
- 经典的hopfield神经网络的源程序，With with application in Ap:Autoassociative Memory Associative Recall of Images-classical neural network source, with application in With Ap : Autoassociative Associative Memory Recall of Images
precision_
- 对比分类器的测试数据和输出结果，给出Precsion，Recall和F-Measure-contrast classification of the test data and output the results, given Precsion. Recall and F-Measure
svm_perf.tar
- New training algorithm for linear classification SVMs that can be much faster than SVMlight for large datasets. It also lets you direcly optimize multivariate performance measures like F1-Score, ROC-Area, and the Precision/Recall Break-Even Point.
准确率召回率及f值的计算源码
- 准确率召回率及f值的计算源码，本代码主要用于上述三个数值的计算，主要针对自然语言处理领域检索结果。-the values of the F and the values of Recall and the precession 。the program is offen used in the area of NLP .
DM4
- 执行流程： 1. 用户输入参数：K的选择，训练数据，测试数据的路径； 2. 读取训练数据集和测试数据集文件，用ArffFileReader类读取并组织起InstanceSet数据结构； 3. 利用上面的相似度量标准，对每一个测试集中的Instance，计算与其最相似的K个训练集中的Instance，通过投票进行分类，将分类结果存储经Instance的成员变量targetGuess中； 4. 对分类结果进行度量，包括分类正确率，各种类别实例的Precision，Recall；Con
bpsuanfa
- 模式分类，BP算法，给出查全率和差准率，对隐含层书目进行讨论-Pattern classification, BP algorithm, given recall rate and the poor precision of the hidden layer bibliographic discussion
AI_Blood
- 本次大作业利用K‐近邻（K‐Nearest Neighbor）算法，为给定的训练数据集构造了分类器， 并在测试数据集上进行分类预测，同时计算了Accuracy、Precision、Recall和F‐measure，利用 10‐fold的实验方法进行交叉验证。-The big job to use K-neighbor (K-Nearest Neighbor) algorithm, for a given set of training data classifier is constru
facebpn
- This program will give better precision and recall rate as this coding uses Back propagation network
E2LSH-0.1
- 局部敏感哈希算法进行数据查找和召回。可以实现最近邻查找、局部敏感哈希算法查找，并且比较两者的召回率。-Local sensitive hashing algorithm to find and recall data. Find the nearest neighbor can be achieved, local sensitive hash algorithm to find and compare the recall.
PLA_initial
- 感知机算法基础实现 带Accuracy、Recall、Precision、F1 的计算-Perceptron algorithm underlying implementation with Accuracy, Recall, Precision, calculation of F1
PLA_pocket
- PLA口袋算法实现 包含Accuracy、recall、precision、F1四大指标的验证-Verify PLA pocket algorithm contains Accuracy, recall, precision, F1 four indicators
tiny-yolov3-master
- CSDN-tiny YOLO v3做缺陷检测实战。九月份用tiny-yolo v3做了一个缺陷检测的实验，效果出乎意料，准确率和召回率“满分”！(CSDN tiny Yolo V3 is used for defect detection. In September, we did a defect detection experiment with tiny Yolo V3, and the effect was unexpected. The accuracy and recall rate