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Srinivas_Mukkamala
- 两种SVM特征选择方法，用PPT详细的阐述了作者的思路和实验情况，非常清除明白。-two feature selection method, the PPT detailed descr iption of the author's ideas and experiments, understand very clear.
shenjingwangluo
- 里面有两个实验，包括PPT和实验的代码，可以帮助一些想学习神经网络的朋友一个大忙。-We now have an easy scr ipt (easy.py) for users who know NOTHING about svm. It makes everything automatic--from data scaling to parameter selection. The parameter selection tool grid.py generates the follo
emd
- 关于emd经验模式分解的幻灯片，里面给出了多了emd过程，求出imf-emd-imf
svm-Intrusion-Detection
- SVM用于入侵检测的PPT，适合初学者，有关于SVM理论的介绍，入门。-SVM for intrusion detection of PPT, suitable for beginners, an introduction on the SVM theory, Getting Started.
ATR-7-SVM
- SVM算法的介绍性PPT，主要介绍SVM算法以及编程举例。-SVM algorithm introductory PPT, introduces the SVM algorithm and programming examples.
Study-of-SVM
- 学习SVM算法很好的一个ppt，建议大家看看。-SVM Algorithm
SVM
- PPT file for support vector machine.It is easy to understand.
svm-struct
- 支持向量机结构回归算法（svm-struct），一种新的回归方法，有文献和PPT，方便学习。-Support the structural regression algorithm vector machine (svm-struct), a new regression method, literature and PPT to facilitate learning.
SVM
- SVM支持向量机的教程，包含PPT，欢迎下载-SVM support vector machine tutorial, including PPT, welcome to download
intelligent-pattern-recognition-ppt
- 自动化学院模式识别课件，SVM算法，几何分类器专题，智能模式识别-Courseware automation institute of pattern recognition, the SVM algorithm, geometrical classifier project, intelligent pattern recognition
SVM
- 这里面包含了两个文件，主要是介绍SVM的，其中配以源程序和相应的PPT，让大家能够很快的掌握SVM-It contains two files, mainly to introduce the SVM, which together with the source code and the corresponding PPT, so that we can quickly grasp SVM
svmtutorial
- SVM支持向量机的详细讲解，值得一读，内容是ppt格式-SVM introduction, detailed
svm
- 自己收集整理的一些svm基础知识，做成ppt的形式，希望对同为初学者的小伙伴们有帮助-collectting some basic knowledge of SVM, the form of PPT, hope to help friends with for beginners
Segmentation-of-Brain-Tumour-by-SVM-Classifier-Te
- ppt for braintumour classification using svm classifier
fSVMppt
- PPT explaining fuzzy SVM
libsvm-3.11
- svm,支持向量机的算法实例及数据等还有ppt,matlab平台测试(SVM, support vector machine algorithms, examples and data, as well as PPT, Matlab platform testing)