登录
首页 » WINDOWS » AdaBoost算法

AdaBoost算法

于 2017-12-23 发布 文件大小:7745KB
0 245
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

  • LMS_RLS
    拟使用基于LMS与RLS的自适应算法在MATLAB平台上对带有两个权的自适应线性组合器进行仿真,进而对两类算法的性能作比较，同时也考察了两种算法在不同参数条件下曲线收敛性的变化(Intending to use the LMS and RLS-based adaptive algorithm in the MATLAB platform with two pairs of the right to self-adaptive linear combiner is simulated, and thus the performance of two types of algorithms to compare, but also examines two kinds of algorithms with different parameters under the conditions of convergence of the change curve)
    2010-03-08 22:40:05下载
    积分：1
  • SRM
    project on SRM drive system The file is a Matlab m-file with all of the input parameters for the two Simulink models and It must be run prior to running the Simulink models to create the input parameters in the Matlab workspace. Because it is a Matlab m-file, it can be opened to change model input parameters. Once you have changed a parameter in you must run it again before you run the Simulink model to update the Matlab workspace and have the change take effect. The file is a Matlab m-file with all of the input parameters for the Simulink model.
    2013-08-03 08:35:11下载
    积分：1
  • program
    动态K-S检验边缘分布的copula 分布估计算法( Marginal distribution in copula estimation of distribution algorithm based dynamic K-S test)
    2013-11-18 19:02:56下载
    积分：1
  • MinLM1
    说明：  非线性最小二乘优化问题 非线性最小二乘优化问题的程序，欢迎各位下载(Nonlinear least squares optimization problem of nonlinear least square optimization procedure, to welcome you to download)
    2011-04-12 19:24:02下载
    积分：1
  • a
    用差分方法计算带时变系数的波动方程,界面可视。(simulate the wave functions with time-varying coefficients)
    2013-10-29 11:32:19下载
    积分：1
  • TE_model2d
    利用二维时域有限差分法纵向TE波模拟钻井(Using two-dimensional finite difference time domain simulation of drilling TE wave)
    2010-10-16 11:34:29下载
    积分：1
  • MATLAB教室人数统计（界面GUI，详细注释，论文）
    说明：  MATLAB教室人数统计（含界面GUI，详细注释，论文） 运行方式 matlab命令窗口输入guide回车 选择fig文件，选择改变路径 运行即可 可二次开发成教室考勤系统，识别出教室里面的人是谁。(Matlab classroom number statistics (including GUI interface, detailed notes, papers) Operation mode Matlab command window input guide enter Select fig file and change path Can run It can be developed into a classroom attendance system to identify who is in the classroom.)
    2021-04-28 09:48:44下载
    积分：1
  • jiong--erjinzhi
    囧子图动画的设计以及二进制GUI界面的设计，很实用(囧 subgraph animation design and the design of binary GUI interface, very useful)
    2012-05-03 15:42:05下载
    积分：1
  • cross-array
    十字阵的波束形成图，包括相关的三维图，极坐标图等(Cross array beam forming diagram, including a three-dimensional view of the polar diagram)
    2012-10-22 21:16:14下载
    积分：1
  • calculator
    说明：  matlab GUI 计算器，和附件的计算器功能类似，由GUI实现(matlab GUI calculator, calculator functions and accessories similar to the GUI to achieve)
    2011-04-08 16:59:26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！