Spotting Method for Classification of Real World Data
Spotting Method for Classification of Real World Data
复制标题
现实世界数据分类的发现方法
DOI：
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发表时间：
1998
期刊：
影响因子：
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通讯作者：
R. Oka
中科院分区：
文献类型：
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作者：
R. Oka
本文提出的情况下，现场计算方案，从而产生了一个新的分类方法处理真实的世界数据的测量算法下开发的真实的世界计算（RWC）计划和相关工作在日本。定位函数具有无分割特性，其优雅地忽略不属于任务域的大多数真实的输入数据。在RWC计划下开发了一些点样方法家族的成员。本文展示了一些发现方法如何应对为它们提出的挑战。点样方法之间的共同计算结构表明了点样计算的架构。
This paper makes the case for a spotting computation scheme which gives rise to a new classification methodology for processing real world data by surveying algorithms developed under the Real World Computing (RWC) program and related work in Japan. A spotting function has the segmentation-free characteristic which ignores gracefully most real world input data which do not belong to a task domain. Some members of the family of spotting methods have been developed under the RWC program. This paper shows how some spotting methods rise to the challenge of the case made for them. The common computational structure amongst spotting methods suggests an architecture for spotting computation.