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Description: adaboost分类算法的C++实现，可直接运行-adaboost classification algorithm C++ achieve, can be directly run
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Author: panshengnan |
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Description: 用matlab实现的adaboost分类器算法Implemented-Implemented with the matlab adaboost Classifier
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Author: zhch65 |
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Description: 用于机器学习的全方位python代码，包括K-近邻算法、决策树、朴素贝叶斯、Logistic 回归 、支持向量机、利用 AdaBoost 元算法提高分类性能、预测数值型数据：回归、树回归、利用 K-均值聚类算法对未标注数据分组、使用 Apriori 算法进行关联分析、使用 FP-growth 算法来高效分析频繁项集、利用 PCA 来简化数据、利用 SVD 简化数据、大数据与 MapReduce-The full range of python code for machine learning. Including K-Nearest Neighbor Algorithm, Decision Tree, Naive Bayes, Logistic Regression, Support Vector Machine, AdaBoost Meta-algorithm to improve the classification performance,etc
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Author: 杨宇 |
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Description: [原创]混沌分析,聚类分析,支持向量机,群体智能优化,深度学习(卷积神经网络)Matlab工具箱全开源版本下载
作者: 陆振波
毕业院校：海军工程大学，船舶与海洋工程(水声工程)，博士
精通方向：信号处理，图像处理，人工智能，模式识别，支持向量机，深度学习，机器学习，机器视觉，群体智能，非线性与混沌，Matlab与VC++混编，大数据
擅长技能：团队激励，战略规划，企业文化，组织架构，C，C++，Matlab，OpenCV，并行计算，图像处理，智能视觉，卷积神经网络，人脸检测，行人检测，车牌识别，特征提取，无人驾驶，自动驾驶，毫米波雷达，激光雷达，辅助驾驶，ADAS，AdaBoost，LBP，HOG，MeanShift，目标检测，目标识别，目标跟踪，数据挖掘，大数据
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微信：luzhenbo2
个人博客: http://blog.sina.com.cn/luzhenbo2
欢迎同行切磋交流，同时请自我介绍，内容包括：姓名、所在城市、所在单位，工作职责等。-[Original] chaos analysis, cluster analysis, support vector machine, group intelligent optimization, depth learning (convolution neural network) Matlab toolbox full open source version Download
Author: Lu Zhenbo
Graduated from: Naval University of Engineering, Marine and Marine Engineering (Underwater Engineering), Ph.D.
Depth of learning, machine learning, machine vision, group intelligence, nonlinear and chaos, Matlab and VC++ mixed, large data (1), and a large number of data processing,
Image processing, intelligent vision, convolutional neural network, face detection, pedestrian detection, license plate recognition, feature extraction, image processing, image processing, image processing, ADAS, AdaBoost, LBP, HOG, MeanShift, target detection, target recognition, target tracking, data mining, large data, radar, radar, autopilot, millimeter wave radar
E-mail: 41670240@qq.com
WeChat: luzhenbo2
Personal blog: http://blog.sina.com.cn/luzhenbo2
Welcome to peer exchange, and pleas
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Size: 1847296 |
Author: 陆振波 |
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Description: 利用opencv的adaboost算法进行人脸检测android源码-Using AdaBoost opencv algorithm for human face detection Android source code
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Size: 626688 |
Author: liu |
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Description: 场景图像中文本占据的范围一般都较小，图像中存在着大范围的非文本区域。因此，场景图像文本定位作为一个独立步骤越来越受到重视。这包括从最先的CD和杂志封面文本定位到智能交通系统中的车牌定位、视频中的字幕提取，再到限制条件少，复杂背景下的场景文本定位。与此同时文本定位算法的鲁棒性越来越高，适用的范围也越来越广泛。文本定位的方式一般可以分为三种，基于连通域的、基于学习的和两者结合的方式。基于连通域的流程一般是首先提取候选文本区域，然后采用先验信息滤除部分非文本区域，最后根据候选文本字符间的关系构造文本词。基于学习的方式关键在于两个方面：一是不同特征提取方法的使用如纹理、小波、笔画等。二是分类器的使用如支持向量机(Support Vector Machine，SVM)，AdaBoost等。连通域和学习结合的方式一般在提取阶段采用连通域的方式，但是滤除阶段是通过训练样本学习分类器来实现非文本的滤除。-The range of the text in the scene image is generally small, and there exists a wide range of non-text areas in the image. Therefore, the scene image text positioning as an independent step more and more attention. This includes positioning text the first CD and magazine cover to license plate location in intelligent transportation systems, capturing subtitles in video, and then locating scene text in complex backgrounds with fewer constraints. At the same time, the robustness of the text localization algorithm is more and more high, and the range of application is more and more extensive. Text localization methods can be divided into three types, based on the connectivity domain, based on learning and a combination of the two methods. The process of the connectivity domain is usually to extract the candidate text area first, then to filter some non-text areas by prior information, and finally construct the text words according to the relationship between candidate text characters. Le
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Author: 折胜军 |
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Description: 分类识别，结合AdaBoost算法，有一定实验效果，但有待于进一步改进-Classification and recognition, combined with AdaBoost algorithm, have some experimental results, but need to be further improved
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Size: 9216 |
Author: 李建伟 |
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Description: adaboost算法中用于计算haar特征值的代码-The code used in the adaboost algorithm to compute haar eigenvalues
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Size: 6144 |
Author: mr-li |
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Description: 集成学习将若干基分类器的预测结果进行综合，具体包括Bagging算法和AdaBoost算法；还有随机森林算法，利用多棵树对样本进行训练并预测的一种分类器-Integrated learning integrates the prediction results of several base classifiers, including Bagging algorithm and AdaBoost algorithm and random forest algorithm, using a tree to train the sample and predict a classifier
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Size: 2048 |
Author: 董小鱼 |
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Description: 利用opencv，adaboost+haar特征实现人脸识别的python小程序-a program use opencv and haar feature to detect face a picture
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Size: 253952 |
Author: 苟利国 |
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Description: 边缘检测，基于adaboost的边缘检测，最优化边缘检测-edge detection
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Size: 2048 |
Author: 蒲轮雨山 |
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Description: 本书第一部分主要介绍机器学习基础，以及如何利用算法进行分类，并逐步介绍了多种经典的监督学习算法，如k近邻算法、朴素贝叶斯算法、Logistic回归算法、支持向量机、AdaBoost集成方法、基于树的回归算法和分类回归树（CART）算法等。第三部分则重点介绍无监督学习及其一些主要算法：k均值聚类算法、Apriori算法、FP-Growth算法。第四部分介绍了机器学习算法的一些附属工具。
全书通过精心编排的实例，切入日常工作任务，摒弃学术化语言，利用高效的可复用Python代码来阐释如何处理统计数据，进行数据分析及可视化。通过各种实例，- U672C u4E66 u7B2C u4E00 u90E8 u5206 u5206 u4E3B u8991 u4ECB u7ECD u673A u5668 u5B66 u4E60 u57FA u7840 uFF0C u4EE5 u53CA u5982 u4F55 u5229 u7528 u7B97 u6CD5 U8FDB u883C u5206 u7C7B uFF0C u5E76 u9010 u6B65 u4ECB u7ECD u4E86 u591A U90BB u6CD2 u3001 u6734 u7D2 u8D1D u53F6 u65AF u7B97 u6CD5 u3001Logistic u56DE u5F52 u7B97 u6CD5 u3001 u652F u6301 u5411 u91CF u673A u3001AdaBoost u96C6 u6210 U65B9 u6CD5 u3001 u57FA u4E8E u6811 u7R4 u5F52 u7B97 u6CD2 u5286 u5206 u7C7B u56DE u5F52 u6811 uFF08CART uFF09 u7B97 u6CD5 u7B49 u3002 u7B2C u4E09 U90E8 u5209 u5109 u91A u0312 u4E9B u8E1B u8981 u7B97 u6CD5 uFF1Ak u5747 u503C u805A u7B7B U7B97 u6CD3 u30011 by default for uppercase and uupstr U4E9B u9644 u5C5E u5DE5 u5177 u3002 u5168 u4E66 u901A u8FC7 u7CBE U5FC3 u7396 u7392 u7B09 u5B09 u5B09 u5B09 u5B03 U7528 u9184 u5R4 u5904 u7403 u7R03 u7 U5206 u6790 u53CA u53EF u89
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Size: 52069376 |
Author: 王雪玮 |
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Description: Demo of adaBoost (adaptive boosting)
Xu Cui
2009/05/12
clear
training data (1000 samples, 2 dimensions, can be seen as 1000 people, each people have 2 variable: weight and age)
data = randn(1000,2) 1000 number (x,y) 2d
label = double(data(:,1)>data(:,2)) a linear separation example
label = double((data(:,1).^2 - data(:,2).^2)<1) a nonlinear separation example
label = double((data(:,1).^2 + data(:,2).^2)<1) a nonlinear separation example- Demo of adaBoost (adaptive boosting)
Xu Cui
2009/05/12
clear
training data (1000 samples, 2 dimensions, can be seen as 1000 people, each people have 2 variable: weight and age)
data = randn(1000,2) 1000 number (x,y) 2d
label = double(data(:,1)>data(:,2)) a linear separation example
label = double((data(:,1).^2 - data(:,2).^2)<1) a nonlinear separation example
label = double((data(:,1).^2 + data(:,2).^2)<1) a nonlinear separation example
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Size: 1024 |
Author: a |
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Description: 基于dsp的人脸识别，使用harr特征和adaboost级联分类器(Based on dsp face recognition, use the harr feature and the adaboost cascade classifier)
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Size: 3505152 |
Author: devil00
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Description: Virtual power wireless sensor network coverage, Using matlab written narrowband noise occurs, Spectral methods of computational fluid dynamics flow of some of the overall stability of the phenomenon.
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Size: 4096 |
Author: taomanfiu
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Description: A complete set of brothers, Relief computing classification weight, Gaussian white noise generator.
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Size: 9216 |
Author: laimingpieyao
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Description: Including compression ratio, image restoration computing uptime and peak signal to noise ratio, Machine learning routines, Simulation of doubly fed induction generator system.
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Size: 4096 |
Author: mouqiugou
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Description: 《机器学习实战》通过精心编排的实例，切入日常工作任务，摒弃学术化语言，利用高效可复用的Python代码阐释如何处理统计数据，进行数据分析及可视化。读者可从中学到一些核心的机器学习算法，并将其运用于某些策略性任务中，如分类、预测及推荐等。(As you work through the numerous examples, you'll explore key topics like classification, numeric prediction, and clustering. Along the way, you'll be introduced to important established algorithms, such as Apriori, through which you identify association patterns in large datasets and Adaboost, a meta-algorithm that can increase the efficiency of many machine learning tasks.)
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Author: bfy
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Description: 人脸检测：
第一部分，使用Harr-like特征表示人脸，使用“ 积分图”实现特征数值的快速计算;
第二部分， 使用Adaboost算法挑选出一些最能代表人脸的矩形特征( 弱分类器)，按照加权投票的方式将弱分类器构造为一个强分类器;
第三部分， 将训练得到的若干强分类器串联组成一个级联结构的层叠分类器，级联结构能有效地提高分类器的检测速度。(Face detection:
In the first part, the Harr-like feature is used to represent the human face, and the "integral graph" is used to realize the fast calculation of feature values;
In the second part, the Adaboost algorithm is used to select some rectangular features (weak classifiers) that represent the human face, and the weak classifier is constructed into a strong classifier according to weighted voting;
In the third part, a series of strong classifiers are formed in series to form a cascade classifier with cascading structure, and the cascade structure can effectively improve the detection speed of classifier.)
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Size: 2048 |
Author: 14024235
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Description: 仿真处理光纤数据，一种简单的拟合方法，易学。附赠adaboost算法论文一篇(photonic crystal fiber dispersion fitting curve based on matlab)
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Author: 电科24号
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