Description: 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. Platform: |
Size: 87142 |
Author:张山 |
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Description: 文档聚类评估程序，计算查全率和查准率以、F值，C++编程实现-Document Clustering assessment procedures, calculation, recall and precision rate, the value of F, C Programming Platform: |
Size: 1024 |
Author:rr |
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Description: 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. Platform: |
Size: 87040 |
Author:张山 |
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Description: 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 Platform: |
Size: 1024 |
Author:曹红丽 |
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Description: PASCAL Visual Object Classes，视觉识别竞赛的程序，共有20大类视觉目标。用MATLAB代码写成。能生成precision/recall图。-PASCAL Visual Object Classes, Competition visual recognition procedures, a total of 20 major categories of the visual target. With code written in MATLAB. Able to generate precision/recall graph. Platform: |
Size: 254976 |
Author:yangchengbo |
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Description: 本文提出了一种新的基于对模型的分块直方图求交互信息量的镜头检测算法。算法中对每帧图象
进行分块求直方图, 然后利用相邻帧间对应分块的直方图统计值求交互信息量, 最后把所有分块的交互信息量进行
加权平均以检测镜头的变化。实验结果表明与传统的直方图相比, 该算法对一般场景的镜头变化有更高的查全率和
查准率。-This paper presents a new model based on the sub-block histogram information for cross-detection algorithm of the camera. Algorithm for each image frame block for histogram, and then frame the use of adjacent sub-block histogram corresponding statistics for the amount of information interaction, and finally all of the interactive block to the weighted average amount of information to detect changes in lens . The experimental results show that compared with the traditional histogram, the algorithm for general scenes of the lens changes in higher recall rate and precision. Platform: |
Size: 349184 |
Author:丁金金 |
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Description: 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；Confusion Matirx；
-Implementation process: 1. User input parameters: K' s choice, the training data, test data path 2. To read the training data set and test data sets document, read and use ArffFileReader category InstanceSet organize the data structure 3. The use of the above similarity metrics for each concentration of a test Instance, the calculation of the K most similar training focused Instance, by voting for classification, the classification results stored by a member of Instance variables in targetGuess 4. on the classification results of measurement, including the classification accuracy of various types of examples of Precision, Recall Confusion Matirx Platform: |
Size: 130048 |
Author:xsl |
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Description: 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 constructed, and the test data sets to classify forecasts, while the calculation of the Accuracy, Precision, Recall and F-measure , using 10-fold cross-validation of experimental methods. Platform: |
Size: 1010688 |
Author:andy |
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Description: Content-based medical image retrieval is now getting more and more attention in the
world, a feasible and efficient retrieving algorithm for clinical endoscopic images is urgently
required. Methods: Based on the study of single feature image retrieving techniques, including color
clustering, color texture and shape, a new retrieving method with multi-features fusion and relevance
feedback is proposed to retrieve the desired endoscopic images. Results: A prototype system is set
up to evaluate the proposed method’s performance and some evaluating parameters such as the
retrieval precision & recall, statistical average position of top 5 most similar image on various features, etc.
are therefore given. Conclusions: The algorithm with multi-features fusion and relevance feedback
gets more accurate and quicker retrieving capability than the one with single feature image retrieving
technique due to its flexible feature combination and interactive relevance feedback. Platform: |
Size: 359424 |
Author:gokul/goks |
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Description: Creating precision/recall plots
psbPlot provides a program for generating precision-recall plots from a classification and a distance matrix. Plots can be created for each model, each class, or an overall average (the default). Run the program as psbPlot.exe classification.cla method.matrix [-macro|-class|-model] Platform: |
Size: 38912 |
Author:dss |
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Description: how to normalize a data, calculation of local measure, precision recall formula,-how to normalize a data, calculation of local measure, precision recall formula, Platform: |
Size: 2048 |
Author:sajuvarghese |
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Search - precision recall