什么是svm技术的基础 [英] what is the basic of svm technic

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问题描述

我想用svm.i进行人脸识别的一个项目想知道怎么做??,svm的基本原理是什么?????????在java中编码是可能的???这是与svm相当的另一种训练方法????这是efitiont方法????请求教我.............

解决方案

正如Wes所说,你必须做大量的研究和研究。 />


如果SVM是指支持向量机,那么古老的维基百科可以帮助你入门。

http://en.wikipedia.org/wiki/Support_vector_machine [ ^ ]

查看参考部分。



您可以在这里获得一些信息以及

http:// onionesquereality .wordpress.com / 2008/08/31 / demystifying-support-vector-machines-for-beginners / [ ^ ]



或者你可以打字e 在您最喜爱的搜索引擎中支持向量机代码并查看结果。


SVM的基本内容根本不是基本的,它是众多先进的机器学习算法之一,属于一种用于模式分类和回归问题的前馈网络。它可以处理线性和非线性可分离模式。 blah ... blah ...需要认真和正式的学习才能理解SVM,这是高等教育中众多AI模块之一。不是这个快速答案论坛。建议你参加一个正式的课程(我想你已经在做了),并获得一本关于神经网络的好书。同时,享受视频 [ ^ ]并对SVM的强大功能感到惊讶。

i want to do one project with in face recognition using svm.i want to know how to do that??,what is basic principle about svm???????? coding with in java is possible ??? which are the another training method equivalent with svm???? which is efitiont method???? pleas teach me.............

解决方案

As Wes says, you have to do a lot of research and studying.

If by SVM you mean Support Vector Machine, good old Wikipedia can get you started.
http://en.wikipedia.org/wiki/Support_vector_machine[^]
Look into the reference section.

You can get some info here as well
http://onionesquereality.wordpress.com/2008/08/31/demystifying-support-vector-machines-for-beginners/[^]

Or you can type Support Vector Machine Code in your favorite search engine and look at the results.


The basic of SVM is NOT basic at all, it is one of the many advanced machine learning algorithms, it belongs to a type of feed forward network for pattern classification and regression problems. It can handle linear as well as non-linear separable patterns. blah...blah... It take serious and formal learning to understand SVM, is one of the many AI modules in the tertiary education. Not for this quick answers forum. Suggest you attend a formal course (I think you are already doing it) and get a good book on neural networks. Meanwhile, enjoy this video [^] and be amazed by the power of SVM.


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