.png){kind=icon}
返回
Extended decision template presentation for combining classifiers
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2011.01.036.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, a new method in classifier fusion is introduced for decision making based on internal structure of base classifiers. Amongst methods used in combining classifiers, there are some methods which work on decision template as a tool for modeling behavior of base classifiers in order to label data. This tool models their behavior only based on their final outputs. Our new method, introduces a special structure for decision template such that internal behavior of a neural network base classifier can be modeled in a proper manner suitable for classifiers fusion. The new method builds decision template for each layer of the neural network including all hidden layers. Therefore, the process of making decision in each base classifier is also available for classifiers fusion. Efficiency of the new method is compared with some known benchmark datasets to show how it can improve efficiency of classifiers fusion. (C) 2011 Elsevier Ltd. All rights reserved.
Keyword:
Decision template
Classifiers fusion
Classifiers combiner
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.5
论文数:
2.9W
被引数:
10.2W
机构
引用论文
.png){kind=icon}
分享收藏分享收藏分享收藏Characterization of soybean fermented by aflatoxin non-producing Aspergillus oryzae and γ-aminobutyric acid producing Lactobacillus brevis不产生黄曲霉毒素的米曲霉和产生 γ-氨基丁酸的短乳杆菌发酵大豆的特性
OAAI
OAAI分享收藏
查看更多内容