Description: 支持向量机初学者可以看看,用于可以用于C-SVM分类、?-SVM分类、?-SVM回归和?-SVM回归等问题
-Support Vector Machine beginners can see that can be used for C-SVM classification,?-SVM classification,?-SVM regression and?-SVM regression problem Platform: |
Size: 827392 |
Author:无为 |
Hits:
Description: 一个Java实现的支持向量机(含源码),SVM算法比较复杂，不过这个程序看起来比较好懂。-Java realization of a support vector machine (including source code), SVM algorithm is rather complicated, but this process appears to be better understood. Platform: |
Size: 433152 |
Author:王晓丹 |
Hits:
Description: 一维支持向量机回归以及二维支持向量机回归-One-dimensional support vector machine regression, as well as two-dimensional support vector machine regression Platform: |
Size: 235520 |
Author:赵星 |
Hits:
Description: 支持向量机，matlab工具集。涵盖关于支持向量机的各种主要算法实现。-Support Vector Machines, matlab tools. Support Vector Machine on the cover of the main algorithm. Platform: |
Size: 4064256 |
Author:风铃 |
Hits:
Description: 这是一个关于最小二乘支持向量机的MATLAB仿真的例子，希望能给大家带来方便。-This is a least squares support vector machine on the MATLAB simulation examples, we hope that they will be convenient. Platform: |
Size: 31744 |
Author:蔡蓓蓓 |
Hits:
Description: SVMstruct is a Support Vector Machine (SVM) algorithm for predicting multivariate or structured outputs. It performs supervised learning by approximating a mapping
h: X --> Y
using labeled training examples (x1,y1), ..., (xn,yn). Unlike regular SVMs, however, which consider only univariate predictions like in classification and regression, SVMstruct can predict complex objects y like trees, sequences, or sets. Examples of problems with complex outputs are natural language parsing, sequence alignment in protein homology detection, and markov models for part-of-speech tagging. The SVMstruct algorithm can also be used for linear-time training of binary and multi-class SVMs under the linear kernel.
-SVMstruct is a Support Vector Machine (SVM) algorithm for predicting multivariate or structured outputs. It performs supervised learning by approximating a mapping
h: X--> Y
using labeled training examples (x1,y1), ..., (xn,yn). Unlike regular SVMs, however, which consider only univariate predictions like in classification and regression, SVMstruct can predict complex objects y like trees, sequences, or sets. Examples of problems with complex outputs are natural language parsing, sequence alignment in protein homology detection, and markov models for part-of-speech tagging. The SVMstruct algorithm can also be used for linear-time training of binary and multi-class SVMs under the linear kernel.
Platform: |
Size: 117760 |
Author:jon |
Hits:
Description: 支持向量机和BP神经网络虽然都可以用来做非线性回归，但它们所基于的理论基础不同，回归的机理也不相同。支持向量机基于结构风险最小化理论，普遍认为其泛化能力要比神经网络的强。为了验证这种观点，本文编写了支持向量机非线性回归的通用Matlab程序和基于神经网络工具箱的BP神经网络仿真模块，仿真结果证实，支持向量机做非线性回归不仅泛化能力强于BP网络，而且能避免神经网络的固有缺陷——训练结果不稳定。
-Support Vector Machine and BP neural network, even though there can be used to make non-linear regression, but they are based on the theoretical basis for the different, the mechanism of regression is not the same. Support vector machine based on structural risk minimization theory, generally considered the generalization ability of neural networks than the strong. To test this view, the paper prepared by non-linear regression support vector machine procedures and based on a common Matlab neural network toolbox of BP neural network Platform: |
Size: 3072 |
Author:孙准 |
Hits:
Description: 支持向量机非线性回归通用MATLAB源码本源码可以用于线性回归、非线性回归、非线性函数拟合、数据建模、预测、分类等多种应用场合-Universal non-linear regression support vector machine MATLAB source code of this source can be used for linear regression, nonlinear regression, nonlinear function approximation, data modeling, prediction, classification and other applications Platform: |
Size: 7168 |
Author:fangcao |
Hits:
Description: SVM向量机相关外文资料的PDF，前几天刚下的，先传上来交流交流-SVM vector machine relevant foreign language material of PDF, just a few days ago, sent up first conversation
Platform: |
Size: 50176 |
Author:叶秋 |
Hits:
Description: 利用谱聚类方法在特
征向量空间中对原始样本数据进行重新表述使得在新表述中同一聚类中的样本能够更好地积聚在一起构建聚类核函数 并进而构造聚类核半监督支持向量机 使样本更好地满足半监督学习必须遵循的聚类假设 -Restated in the new formulation in the same cluster sample be better able to accumulate together to build the clustering of nuclear function and thus to construct the semi-supervised clustering of nuclear support vector method of spectral clustering in the feature vector space of the original sample data machine so that the sample to better meet the needs of semi-supervised learning clustering assumptions that must be followed Platform: |
Size: 155648 |
Author:小白 |
Hits:
Description: Location prediction is a special case of spatial data mining
classification. For instance, in the public safety domain,
it may be interesting to predict location(s) of crime
hot spots. In this study, we present Support Vector Machine
(SVM) based approach to predict the location as alternative
to existing modeling approaches. SVM forms the new generation
of machine learning techniques used to find optimal
separability between classes within datasets. Experiments
on two different spatial datasets show that SVMs gives reasonable
results. Platform: |
Size: 197632 |
Author:ahmed |
Hits:
Search - support vector machine