.png){kind=icon}
返回
Granular support vector machine based on mixed measure
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2012.08.006.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper presents a granular support vector machine learning model based on mixed measure, namely M_GSVM, to solve the model error problem produced by mapping, simplifying, granulating or substituting of data for traditional granular support vector machines (GSVM). For M_GSVM, the original data will be mapped into the high-dimensional space by mercer kernel. Then, the data are divided into some granules, and those mixed granules including more information are extracted and trained by support vector machine (SVM). Finally, the decision hyperplane will be corrected through geometric analyzing to reduced model error effectively. The experiment results on UCI benchmark datasets and Interacting Proteins database demonstrate that the proposed M_GSVM model can improve the generalization performance greatly with high learning efficiency synchronously. (C) 2012 Elsevier B.V. All rights reserved.
Keyword:
Granular support vector machine
Model error
M_GSVM model
Mixed granule
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
Inconsistency-based active learning for support vector machines基于不一致性的支持向量机主动学习.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏Learning rates of support vector machine classifier for density level detection用于密度水平检测的支持向量机分类器的学习率NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏Determination of the spread parameter in the Gaussian kernel for classification and regressionNEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏
分享收藏
查看更多内容