https://en.wikipedia.org/wiki/Support_vector_machine

In machine learning, support vector machines (SVMs, also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier. An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall.

Support vector machine的更多相关文章

  1. 6. support vector machine

    1. 了解SVM 1. Logistic regression 与SVM超平面 给定一些数据点,它们分别属于两个不同的类,现在要找到一个线性分类器把这些数据分成两类.如果用x表示数据点,用y表示类别( ...

  2. 使用Support Vector Machine

    使用svm(Support Vector Machine)中要获得好的分类器,最重要的是要选对kernel. 常见的svm kernel包括linear kernel, Gaussian kernel ...

  3. Support Vector Machine (3) : 再谈泛化误差（Generalization Error）

    目录 Support Vector Machine (1) : 简单SVM原理 Support Vector Machine (2) : Sequential Minimal Optimization ...

  4. Support Vector Machine (2) : Sequential Minimal Optimization

    目录 Support Vector Machine (1) : 简单SVM原理 Support Vector Machine (2) : Sequential Minimal Optimization ...

  5. Support Vector Machine (1) : 简单SVM原理

    目录 Support Vector Machine (1) : 简单SVM原理 Support Vector Machine (2) : Sequential Minimal Optimization ...

  6. 支持向量机 support vector machine

    SVM(support Vector machine) (1) SVM(Support Vector Machine)是从瓦普尼克(Vapnik)的统计学习理论发展而来的,主要针对小样本数据进行学习. ...

  7. A glimpse of Support Vector Machine

    支持向量机(support vector machine, 以下简称svm)是机器学习里的重要方法,特别适用于中小型样本.非线性.高维的分类和回归问题.本篇希望在正篇提供一个svm的简明阐述,附录则提 ...

  8. 支持向量机SVM(Support Vector Machine)

    支持向量机(Support Vector Machine)是一种监督式的机器学习方法(supervised machine learning),一般用于二类问题(binary classificati ...

  9. 机器学习技法：01 Linear Support Vector Machine

    Roadmap Course Introduction Large-Margin Separating Hyperplane Standard Large-Margin Problem Support ...

  10. 支持向量机（Support Vector Machine，SVM）——　线性SVM

      支持向量机(Support Vector Machine,简称 SVM)于 1995 年正式发表,由于其在文本分类任务中的卓越性能,很快就成为机器学习的主流技术.尽管现在 Deep Learnin ...

随机推荐

  1. jquery 事件冒泡 解决 ie firefox 兼容性问题

    <!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"> <html> <hea ...

  2. wp7 xml

    public class DynamicXMLNode : DynamicObject { XElement node; public DynamicXMLNode(XElement node) { ...

  3. 每个人都应该知晓的8项Resharper快捷键

    (此文章同时发表在本人微信公众号“dotNET每日精华文章”) 如果你已经在用Resharper这个编程神器(机器杀手)的话,那么为了进一步提高工作效率熟悉它的一些特殊特性和快捷键那是必须的. Res ...

  4. linux socket 编程（C语言）

    转自:http://blog.csdn.net/piaojun_pj/article/details/5920888 最近看了一些网络编程的书籍,一直以来总感觉网络编程神秘莫测,其实网络编程入门还是很 ...

  5. URI和URL的区别

    这两天在写代码的时候,由于涉及到资源的位置,因此,需要在Java Bean中定义一些字段,用来表示资源的位置,比如:imgUrl,logoUri等等.但是,每次定义的时候,心里都很纠结,是该用imgU ...

  6. eclipse提示信息设置和提示信息操作

    1.提示信息设置 windows->preference->java->Editor->content Assist->Advance,选择需要提示的内容即可.如图所示: ...

  7. env

    shell环境变量以及set,env,export的区别 原文链接 一.shell环境变量的分类以及set env export的区别: set:显示(设置)shell变量 包括的私有变量以及用户变量 ...

  8. java 输入输出流1 FileInputStrem&&FileOutStream

    通过文件输入流读取问价 package unit6; import java.io.FileInputStream; import java.io.FileNotFoundException; imp ...

  9. 状态压缩 UVALive 6068 The Little Girl who Picks Mushrooms (12长春C)

    题目传送门 题意:采蘑菇.现在采了n座山,共5座山,最后要求有三个篮子的蘑菇量是1024的整数倍,丢掉后一直减1024直到不超过1024 分析:n <= 3时直接1024,否则状压枚举哪三个篮子 ...

  10. 2015ACM/ICPC亚洲区长春站 J hdu 5536 Chip Factory

    Chip Factory Time Limit: 18000/9000 MS (Java/Others)    Memory Limit: 262144/262144 K (Java/Others)T ...