Description: 支撑矢量机
class CvSVM : public CvStatModel //继承自基类CvStatModel
{
public:
// SVM type
enum { C_SVC=100, NU_SVC=101, ONE_CLASS=102, EPS_SVR=103, NU_SVR=104 } //SVC是SVM分类器，SVR是SVM回归
// SVM kernel type
-class Support Vector Machine CvSVM : public CvStatModel// inherited from the base class CvStatModel (ed ic :// SVM type enum (C_SVC = 100, NU_SVC = 101, ONE_CLASS = 102, EPS_SVR = 103, NU_SVR = 104)// SVC is SVM classifier, SVR is the return of SVM// SVM kernel type Platform: |
Size: 7168 |
Author:李刚 |
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Description: 基于核分析的多类分类器，支持向量机的多类分类，适合研究学习，欢迎同行下载-Kernel-based analysis of the many types of classifier, support vector machine multi-category classification, suitable for study of learning, welcomed the peer download Platform: |
Size: 417792 |
Author:lwen |
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Description: In this paper, we show how support vector machine (SVM) can be
employed as a powerful tool for $k$-nearest neighbor (kNN)
classifier. A novel multi-class dimensionality reduction approach,
Discriminant Analysis via Support Vectors (SVDA), is introduced by
using the SVM. The kernel mapping idea is used to derive the
non-linear version, Kernel Discriminant via Support Vectors (SVKD).
In SVDA, only support vectors are involved to obtain the
transformation matrix. Thus, the computational complexity can be
greatly reduced for kernel based feature extraction. Experiments
carried out on several standard databases show a clear improvement
on LDA-based recognition Platform: |
Size: 2048 |
Author:sofi |
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Description: SVM(支持向量机)入门 (深入浅出讲解原理)
深入浅出讲解SVM的原理和应用，有点类似于傻瓜式的教学方法，个人觉得很有帮助。
SVM入门（一）SVM的八股简介
SVM入门（二）线性分类器Part 1
SVM入门（三）线性分类器Part 2
SVM入门（四）线性分类器的求解——问题的描述Part1
SVM入门（五）线性分类器的求解——问题的描述Part2
SVM入门（六）线性分类器的求解——问题的转化，直观角度
SVM入门（七）为何需要核函数
SVM入门（八）松弛变量。
SVM入门（九）松弛变量（续）。
SVM入门（十）将SVM用于多类分类。-The principles and applications of SVM (support vector machine) entry (easy to explain the principle) easy to explain SVM, somewhat similar to the fool-teaching people found helpful. The linear classifier SVM Getting Started (a) the SVM' s Bagu Introduction SVM entry (b) linear classifier Part 1 SVM Getting Started (c) Part 2 SVM Getting Started (d) solving linear classifier- a description of the problem Part1 SVM Getting Started (e) the solution of a linear classifier- a description of the problem Part2 SVM Getting Started (f) the solution of the linear classifier- the transformation of the problem, the visual angle SVM Getting Started (g) Why do I need a kernel function SVM Getting Started (h) slack variables . Slack variables of SVM Getting Started (IX) (continued). SVM Getting Started (j) SVM for multi-class classification. Platform: |
Size: 489472 |
Author:colin |
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Description: Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier—a limited, but well-established and comprehensively studied model—and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis Platform: |
Size: 1024 |
Author:hera |
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Description: 对最小二乘支持向量机的核函数进行参数优化，最后得到分类更加准确的分类器-For least squares support vector machine (SVM) kernel function parameter optimization, finally get a more accurate classification of classifier Platform: |
Size: 2048 |
Author:刘军 |
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Description: Improving Support Vector Machine classifier with different kernel function for plant diseases detection v 2 Platform: |
Size: 92160 |
Author:mokhtar |
Hits:
Description: AN MR BRAIN IMAGES CLASSIFIER VIA PRINCIPAL
COMPONENT ANALYSIS AND KERNEL SUPPORT
VECTOR MACHINE Platform: |
Size: 474112 |
Author:lingam
|
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