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AdaBoost算法

于 2017-12-23 发布 文件大小:7745KB
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下载积分: 1 下载次数: 17

代码说明:

  用matlab软件,实现adaboost算法。将数据集划分为训练集和测试集,给训练集的数据贴标签,用训练好的模型来测试测试数据的准确度。(Using Matlab to implement the AdaBoost algorithm. The data set is divided into training set and test set to label the data of the training set, and the accuracy of the test data is tested by the trained model.)

文件列表:

adaBoost-master
adaBoost-master\README, 604, 2017-01-03
adaBoost-master\bestLinearClassifier.in, 6705, 2017-01-03
adaBoost-master\classifyExample.m, 1157, 2017-01-03
adaBoost-master\computeIntegralImage.m, 1076, 2017-01-03
adaBoost-master\dataset
adaBoost-master\dataset\TestImages
adaBoost-master\dataset\TestImages\test-0.pgm, 24165, 2017-01-03
adaBoost-master\dataset\TestImages\test-1.pgm, 37690, 2017-01-03
adaBoost-master\dataset\TestImages\test-10.pgm, 30015, 2017-01-03
adaBoost-master\dataset\TestImages\test-100.pgm, 20840, 2017-01-03
adaBoost-master\dataset\TestImages\test-101.pgm, 10850, 2017-01-03
adaBoost-master\dataset\TestImages\test-102.pgm, 10850, 2017-01-03
adaBoost-master\dataset\TestImages\test-103.pgm, 34815, 2017-01-03
adaBoost-master\dataset\TestImages\test-104.pgm, 42043, 2017-01-03
adaBoost-master\dataset\TestImages\test-105.pgm, 37255, 2017-01-03
adaBoost-master\dataset\TestImages\test-106.pgm, 21071, 2017-01-03
adaBoost-master\dataset\TestImages\test-107.pgm, 16655, 2017-01-03
adaBoost-master\dataset\TestImages\test-108.pgm, 39075, 2017-01-03
adaBoost-master\dataset\TestImages\test-109.pgm, 23325, 2017-01-03
adaBoost-master\dataset\TestImages\test-11.pgm, 30015, 2017-01-03
adaBoost-master\dataset\TestImages\test-110.pgm, 39117, 2017-01-03
adaBoost-master\dataset\TestImages\test-111.pgm, 16185, 2017-01-03
adaBoost-master\dataset\TestImages\test-112.pgm, 18936, 2017-01-03
adaBoost-master\dataset\TestImages\test-113.pgm, 30612, 2017-01-03
adaBoost-master\dataset\TestImages\test-114.pgm, 10088, 2017-01-03
adaBoost-master\dataset\TestImages\test-115.pgm, 29902, 2017-01-03
adaBoost-master\dataset\TestImages\test-116.pgm, 20840, 2017-01-03
adaBoost-master\dataset\TestImages\test-117.pgm, 41375, 2017-01-03
adaBoost-master\dataset\TestImages\test-118.pgm, 19335, 2017-01-03
adaBoost-master\dataset\TestImages\test-119.pgm, 9854, 2017-01-03
adaBoost-master\dataset\TestImages\test-12.pgm, 14714, 2017-01-03
adaBoost-master\dataset\TestImages\test-120.pgm, 13314, 2017-01-03
adaBoost-master\dataset\TestImages\test-121.pgm, 21591, 2017-01-03
adaBoost-master\dataset\TestImages\test-122.pgm, 25923, 2017-01-03
adaBoost-master\dataset\TestImages\test-123.pgm, 12302, 2017-01-03
adaBoost-master\dataset\TestImages\test-124.pgm, 18108, 2017-01-03
adaBoost-master\dataset\TestImages\test-125.pgm, 20805, 2017-01-03
adaBoost-master\dataset\TestImages\test-126.pgm, 10639, 2017-01-03
adaBoost-master\dataset\TestImages\test-127.pgm, 17664, 2017-01-03
adaBoost-master\dataset\TestImages\test-128.pgm, 19450, 2017-01-03
adaBoost-master\dataset\TestImages\test-129.pgm, 40731, 2017-01-03
adaBoost-master\dataset\TestImages\test-13.pgm, 50715, 2017-01-03
adaBoost-master\dataset\TestImages\test-130.pgm, 38295, 2017-01-03
adaBoost-master\dataset\TestImages\test-131.pgm, 28923, 2017-01-03
adaBoost-master\dataset\TestImages\test-132.pgm, 52750, 2017-01-03
adaBoost-master\dataset\TestImages\test-133.pgm, 16718, 2017-01-03
adaBoost-master\dataset\TestImages\test-134.pgm, 13294, 2017-01-03
adaBoost-master\dataset\TestImages\test-135.pgm, 22387, 2017-01-03
adaBoost-master\dataset\TestImages\test-136.pgm, 15214, 2017-01-03
adaBoost-master\dataset\TestImages\test-137.pgm, 18195, 2017-01-03
adaBoost-master\dataset\TestImages\test-138.pgm, 13738, 2017-01-03
adaBoost-master\dataset\TestImages\test-139.pgm, 13214, 2017-01-03
adaBoost-master\dataset\TestImages\test-14.pgm, 24315, 2017-01-03
adaBoost-master\dataset\TestImages\test-140.pgm, 23815, 2017-01-03
adaBoost-master\dataset\TestImages\test-141.pgm, 16882, 2017-01-03
adaBoost-master\dataset\TestImages\test-142.pgm, 17834, 2017-01-03
adaBoost-master\dataset\TestImages\test-143.pgm, 27075, 2017-01-03
adaBoost-master\dataset\TestImages\test-144.pgm, 13008, 2017-01-03
adaBoost-master\dataset\TestImages\test-145.pgm, 16075, 2017-01-03
adaBoost-master\dataset\TestImages\test-146.pgm, 19417, 2017-01-03
adaBoost-master\dataset\TestImages\test-147.pgm, 13078, 2017-01-03
adaBoost-master\dataset\TestImages\test-148.pgm, 15211, 2017-01-03
adaBoost-master\dataset\TestImages\test-149.pgm, 14467, 2017-01-03
adaBoost-master\dataset\TestImages\test-15.pgm, 44107, 2017-01-03
adaBoost-master\dataset\TestImages\test-150.pgm, 29499, 2017-01-03
adaBoost-master\dataset\TestImages\test-151.pgm, 12020, 2017-01-03
adaBoost-master\dataset\TestImages\test-152.pgm, 14456, 2017-01-03
adaBoost-master\dataset\TestImages\test-153.pgm, 17752, 2017-01-03
adaBoost-master\dataset\TestImages\test-154.pgm, 17823, 2017-01-03
adaBoost-master\dataset\TestImages\test-155.pgm, 16015, 2017-01-03
adaBoost-master\dataset\TestImages\test-156.pgm, 18915, 2017-01-03
adaBoost-master\dataset\TestImages\test-157.pgm, 15204, 2017-01-03
adaBoost-master\dataset\TestImages\test-158.pgm, 16547, 2017-01-03
adaBoost-master\dataset\TestImages\test-159.pgm, 27485, 2017-01-03
adaBoost-master\dataset\TestImages\test-16.pgm, 24027, 2017-01-03
adaBoost-master\dataset\TestImages\test-160.pgm, 22521, 2017-01-03
adaBoost-master\dataset\TestImages\test-161.pgm, 17823, 2017-01-03
adaBoost-master\dataset\TestImages\test-162.pgm, 17151, 2017-01-03
adaBoost-master\dataset\TestImages\test-163.pgm, 20307, 2017-01-03
adaBoost-master\dataset\TestImages\test-164.pgm, 17915, 2017-01-03
adaBoost-master\dataset\TestImages\test-165.pgm, 37551, 2017-01-03
adaBoost-master\dataset\TestImages\test-166.pgm, 18954, 2017-01-03
adaBoost-master\dataset\TestImages\test-167.pgm, 18936, 2017-01-03
adaBoost-master\dataset\TestImages\test-168.pgm, 18915, 2017-01-03
adaBoost-master\dataset\TestImages\test-169.pgm, 15534, 2017-01-03
adaBoost-master\dataset\TestImages\test-17.pgm, 43755, 2017-01-03
adaBoost-master\dataset\TestImages\test-18.pgm, 63375, 2017-01-03
adaBoost-master\dataset\TestImages\test-19.pgm, 47790, 2017-01-03
adaBoost-master\dataset\TestImages\test-2.pgm, 15764, 2017-01-03
adaBoost-master\dataset\TestImages\test-20.pgm, 16674, 2017-01-03
adaBoost-master\dataset\TestImages\test-21.pgm, 19205, 2017-01-03
adaBoost-master\dataset\TestImages\test-22.pgm, 31505, 2017-01-03
adaBoost-master\dataset\TestImages\test-23.pgm, 22615, 2017-01-03
adaBoost-master\dataset\TestImages\test-24.pgm, 24265, 2017-01-03
adaBoost-master\dataset\TestImages\test-25.pgm, 16845, 2017-01-03
adaBoost-master\dataset\TestImages\test-26.pgm, 38655, 2017-01-03
adaBoost-master\dataset\TestImages\test-27.pgm, 27510, 2017-01-03
adaBoost-master\dataset\TestImages\test-28.pgm, 27037, 2017-01-03
adaBoost-master\dataset\TestImages\test-29.pgm, 22524, 2017-01-03

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    了一些资料,都没有说这个仿真平台是什么,是matlab还是直接在VC++的编译环境编程?还是有专门的仿真环境呢? 3、如果用实物验证的话,我觉得有两种方式:一是用摄像头来采集整个地图信息,智能车的位置也有摄像头来反馈;二是,整个地图保存在内存中,小车根据传感器信息自我定位,然后和保存的地图比较。(You can download the current points to 1, the source download get points one day, you can download up to 20 times. [View your upload log] [continue to upload source to increase points] You have recently downloaded the 240 source code, you should contribute something new other friends 【Source】 it? Joined the group】 【QQ, blowing boasting a chat. You can also create a QQ group you join the union,)
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