Description: 这里实现了基于四种SVM工具箱的分类与回归算法:
1、工具箱：LS_SVMlab
Classification_LS_SVMlab.m - 多类分类
Regression_LS_SVMlab.m - 函数拟合
2、工具箱：OSU_SVM3.00
Classification_OSU_SVM.m - 多类分类
3、工具箱：stprtool\svm
Classification_stprtool.m - 多类分类
4、工具箱：SVM_SteveGunn
Classification_SVM_SteveGunn.m - 二类分类
Regression_SVM_SteveGunn.m - 函数拟合
更详细的相关函数说明请通过help命令查看！-Here the realization of the four SVM toolbox based on the classification and regression algorithm: 1, Toolbox: LS_SVMlabClassification_LS_SVMlab.m- Multiclass Classification Regression_LS_SVMlab.m- function fitting 2, the toolbox: OSU_SVM3.00Classification_OSU_SVM.m- Multiclass Classification 3, Toolbox: stprtoolsvmClassification_stprtool.m- Multiclass Classification 4 toolbox: SVM_SteveGunnClassification_SVM_SteveGunn.m- II Category Regression_SVM_SteveGunn.m- function fitting a more detailed explanation of the correlation function through the help command to view! Platform: |
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Author:杨愚根 |
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Description: 模式识别学习综述.该论文的英文参考文献为303篇.很有可读价值.Abstract— Classical and recent results in statistical pattern
recognition and learning theory are reviewed in a two-class
pattern classification setting. This basic model best illustrates
intuition and analysis techniques while still containing the essential
features and serving as a prototype for many applications.
Topics discussed include nearest neighbor, kernel, and histogram
methods, Vapnik–Chervonenkis theory, and neural networks. The
presentation and the large (thogh nonexhaustive) list of references
is geared to provide a useful overview of this field for both
specialists and nonspecialists.-Summary of pattern recognition learning. The paper for English 303 references. Very readable value. Abstract-Classical and recent results in statistical patternrecognition and learning theory are reviewed in a two-classpattern classification setting. This basic model best illustratesintuition and analysis techniques while still containing the essentialfeatures and serving as a prototype for many applications.Topics discussed include nearest neighbor, kernel, and histogrammethods, Vapnik-Chervonenkis theory, and neural networks. Thepresentation and the large (thogh nonexhaustive) list of referencesis geared to provide a useful overview of this field for bothspecialists and nonspecialists. Platform: |
Size: 821248 |
Author:蒋大为 |
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Description: C4.5算法有如下优点：产生的分类规则易于理解，准确率较高。其缺点是：在构造树的过程中，需要对数据集进行多次的顺序扫描和排序，因而导致算法的低效。此外，C4.5只适合于能够驻留于内存的数据集，当训练集大得无法在内存容纳时程序无法运行。-C4.5 algorithm has the following advantages: the classification rules easier to understand, accurate and a higher rate. Its shortcomings are as follows: in the tree structure, the need for a number of data sets the order of scanning and sorting, thus leading to inefficient algorithms. In addition, C4.5 can only be applied to the presence of a data set in memory, when the training set too great to accommodate in memory when the program can not run. Platform: |
Size: 1024 |
Author:xinyuanwo |
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Description: 模式识别ppt。R. O. Duda的那本Pattern Classification 模式识别红宝书配套ppt-Pattern Recognition ppt. RO Duda was the Little Red Book Pattern Classification Pattern Recognition matching ppt Platform: |
Size: 6167552 |
Author:fancyiyang |
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Description: 这个文本分类工具箱给出了有监督和无监督的文本分类算法清单。
这些算法为实验和合成数据提供了设计分类方法。-The text classification toolbox has given supervision and unsupervised text classification algorithm list. These algorithms for the experimental and synthetic data classification method provides a design. Platform: |
Size: 644096 |
Author:庄子 |
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Description: SVDD的工具箱，对一类分类效果非常好。包括圆形和不规则形状的包络面。-SVDD Toolbox, the classification results for a class of very good. Including round and irregular-shaped envelope surface. Platform: |
Size: 436224 |
Author:peng |
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Description: 使用ASP+SQLServer实现在线图书馆管理系统，前台具有查阅书籍，借阅书籍功能，后台则对书籍及其分类进行编辑。
附带数据库
本程序采用B/S架构。-ASP+ SQLServer realize the use of online library management system, prospects have access to books, borrow books features, background on books and their classification for editing. Incidental to the database using this procedure B/S architecture. Platform: |
Size: 112640 |
Author:folishwood |
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Description: 清华版的labview教程12.2.5，讲的很具体详细，分类明确，是初学者比较好的资料-Tsinghua University tutorial labview version 12.2.5, talking about very specific details, a clear classification is relatively good information for beginners Platform: |
Size: 1068032 |
Author:RenYuqi |
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Description: 用于自动选取阈值的功能，可以将一些数值按照一定的方法分类-Used to automatically select the threshold function can be some numerical method of classification according to certain Platform: |
Size: 4096 |
Author:张亦波 |
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Description: 1.GeometricContext文件是完成图片中几何方向目标分类。
参考文献《Automatic Photo Pop-up》Hoiem 2005
2 GrabCut文件是完成图像中目标交互式分割
参考文献《“GrabCut” — Interactive Foreground Extraction using Iterated Graph Cuts》
C. Rother 2004
3 HOG文件是自己编写的根据HOG特征检测行人的matlab代码
4 虹膜识别程序是下载的一个通用的虹膜识别程序，可以运行
5 GML_AdaBoost_Matlab_Toolbox是一个很好用的adaboost matlab工具箱
6 libsvm-mat-2.91-1 是用C编写的改进的SVM程序，代码质量很高，提供了matlab接口
7 SIFT_Matlab 是编写的利用sift特征进行的宽基线匹配，代码质量高
8 FLDfisher 是利用fisher 线性降维方法进行人脸识别-1.GeometricContext file is complete the picture in the geometric direction of target classification. References " Automatic Photo Pop-up" Hoiem 2005 2 GrabCut the target file is an interactive segmentation of image reference " " GrabCut " - Interactive Foreground Extraction using Iterated Graph Cuts" C. Rother 2004 3 HOG documents prepared under their own HOG Characteristics of pedestrian detection matlab code 4 iris recognition process is to download a general iris recognition program, you can run 5 GML_AdaBoost_Matlab_Toolbox is a good use of adaboost matlab toolbox 6 libsvm-mat-2.91-1 is written in C to improve the SVM procedures, code of high quality, provides a matlab interface to 7 SIFT_Matlab is prepared for the use of sift features a wide baseline matching, the code is the use of high quality 8 FLDfisher fisher linear dimension reduction method for face recognition Platform: |
Size: 6918144 |
Author:张数 |
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Description: 各种分类算法都包括在里面,有SVAR,ADMM等,在运用时,只需要调用就可以了。-Various classification algorithms are included in the inside, there SVAR, ADMM, etc., in the use, you only need to call on it. Platform: |
Size: 3937280 |
Author:yuyu |
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