.png){kind=icon}
返回
Solving the slate tile classification problem using a DAGSVM multiclassification algorithm based on SVM binary classifiers with a one-versus-all approach
.png){kind=icon}
PRE
AI
DOI：10.1016/j.amc.2013.12.087.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We describe a new classification methodology based on binary classifiers constructed using support vector machines and applying a one-versus-all approach supported by the use of the directed acyclic graphs. The new methodology, which is computationally less costly because a smaller number of binary classification problems have to be resolved, was validated using UCI Machine Learning Repository data sets. Results point to the improved performance of the proposed model compared to approaches based on the one-versus-one and directed acyclic graph techniques. This new multiclassification strategy successfully applied to a slate tile classification problem produced favourable results compared to other validated techniques. (C) 2013 Elsevier Inc. All rights reserved.
Keyword:
Support vector machines
Directed acyclic graphs
One-versus-all
UCI Machine Learning Repository
Slate tile classification
期刊
IF:
3.4
论文数:
2.3W
被引数:
3.3W
机构
引用论文
Relook at the Mechanisms of Spheroidization of 0.7 wt.% C Steel by Potentiodynamic Polarization
PREAI
分享收藏A scalable support vector machine for distributed classification in ad hoc sensor networks用于ad hoc传感器网络分布式分类的可扩展支持向量机.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏
查看更多内容