登录
首页 » WINDOWS » AdaBoost算法

AdaBoost算法

于 2017-12-23 发布 文件大小:7745KB
0 232
下载积分: 1 下载次数: 17
我要下载

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

下载说明：请别用迅雷下载，失败请重下，重下不扣分！

发表评论

0 个回复

  • MATLAB
    关于matlab编程的书，适合大家入门学习与提高。(Matlab programming on the book, suitable for all entry-learning and to improve.)
    2007-11-29 17:45:51下载
    积分：1
  • test
    MATLAB信号处理程序，用GUI做的。功能全，喊源代码(MATLAB signal processing procedures to do with the GUI.-Function, call the source code)
    2010-05-14 14:32:06下载
    积分：1
  • Qlearningcar
    Simulink 控制VR环境中的小车。小车有5个距离传感器，能够慢慢学会避开墙壁和障碍物。小车采用加强学习（Q learning），采用神经网络对Q函数逼近。由于使用了模拟退火，小车在开始的时候会经常撞击障碍物，10次后基本就不会再撞了。 小车的外观模型使用了"w198406141"在本论坛的虚拟现实区发布的VR模型。(VR environment Simulink control car. There are 5 car distance sensor, can gradually learn to avoid walls and obstacles. Car used to enhance learning (Q learning), using neural networks Q function approximation. As the use of simulated annealing, the car will start when the regular crash barrier, 10 times the fundamental will no longer hit. The appearance model used car " w198406141" in this forum area of virtual reality VR model release.)
    2010-05-20 20:30:10下载
    积分：1
  • p.rar
    很好的实现了LDPC码字的生成，强烈建议使用此生成矩阵进行编码调制(LDPC codeword generated, it is strongly recommended to use this generator matrix code modulation)
    2013-05-07 21:12:06下载
    积分：1
  • hybridColor6
    Hybrid color halftoning algorithm for colored images matlab code
    2015-03-29 14:12:21下载
    积分：1
  • AlSteelNi-UHF
    SHIELDING EFFECTIVENESS OF DIFFERENT MATERIALS AT DIFFERENT FREQUENCY
    2014-09-30 22:10:08下载
    积分：1
  • 燃料电池控制程序
    电池编程，燃料电池的控制程序，适用于matlab，c语言的控制程序(Battery programming, fuel cell control program, suitable for matlab, c language control program)
    2018-11-21 13:27:45下载
    积分：1
  • DespreadFunc
    CDMA中的扩频解扩，用matlab实现(Spread Spectrum CDMA despreading, using Matlab achieve)
    2006-10-12 10:51:53下载
    积分：1
  • MSK
    学会使用MATLAB仿真软件搭建MSK调制解调仿真电路建立MSK调制解调系统仿真模型(Learn how to use MATLAB simulation software to build the MSK modulation and demodulation simulation circuit MSK modulation and demodulation system simulation model)
    2013-01-07 16:36:54下载
    积分：1
  • 6H-SiC-Hexagonal---P63mc(186)-
    分析晶体的TEM衍射斑点与对应的晶面,能快速准确的获得TEM分析结果。(The TEM analysis of crystal diffraction spot with the corresponding crystal plane, and can rapidly and accurately obtain the TEM analysis results.)
    2013-04-07 00:41:46下载
    积分：1
  • 696516资源总数
  • 106914会员总数
  • 0今日下载

热门标签

Visual C++ matlab C/C++ Java Others Unix_Linux EasyLanguage易语言 Visual Basic C# PDF C++ Delphi VHDL ASP WINDOWS
热门下载

Copyright© 2019-2026 | Powered by Imdn | 浙ICP备16007336号 | IMDN交流 | 联系客服（QQ：3324461878）

本网站资源收集自网络及网站用户上传，若有侵权请邮件至（3324461878@qq.com），核实后我们会在24小时内删除，谢谢！