Classifiers under Continuous Observations
Classifiers under Continuous Observations
复制标题
连续观察下的分类器
DOI：
10.1007/3-540-70659-3_84
复制
发表时间：
2002
期刊：
影响因子：
--
通讯作者：
T. Suenaga
中科院分区：
文献类型：
--
作者：
H. Sakano;T. Suenaga
许多研究人员报告说，当将多个图像连续输入识别系统时，识别精度会提高。我们将这种识别方案称为基于连续观察的方案（CObS）。 CObS 不仅是一种有用且强大的目标识别技术，它还为统计模式分类研究提供了新的方向。 CObS 统计模式识别的主要问题是如何定义两个分布之间的相似性度量。在本文中，我们介绍了一些用于连续观察的分类器。我们还通过比较各种分类器来实验证明连续观察的有效性。
Many researchers have reported that recognition accuracy improves when several images are continuously input into a recognition system. We call this recognition scheme acontinuous observation- based scheme(CObS). The CObS is not only a useful and robust object recognition technique, it also offers a new direction in statistical pattern classification research. The main problem in statistical pattern recognition for the CObS is how to define the measure of similarity between two distributions. In this paper, we introduce some classifiers for use with continuous observations. We also experimentally demonstrate the effectiveness of continuous observation by comparing various classifiers.