计算机科学
k-最近邻算法
简单
系列(地层学)
简单(哲学)
时间序列
最近邻搜索
算法
最佳垃圾箱优先
数据挖掘
最近邻图
模式识别(心理学)
人工智能
机器学习
认识论
哲学
古生物学
生物
作者
Lexiang Ye,Eamonn Keogh
标识
DOI:10.1145/1557019.1557122
摘要
Classification of time series has been attracting great interest over the past decade. Recent empirical evidence has strongly suggested that the simple nearest neighbor algorithm is very difficult to beat for most time series problems. While this may be considered good news, given the simplicity of implementing the nearest neighbor algorithm, there are some negative consequences of this. First, the nearest neighbor algorithm requires storing and searching the entire dataset, resulting in a time and space complexity that limits its applicability, especially on resource-limited sensors. Second, beyond mere classification accuracy, we often wish to gain some insight into the data.
科研通智能强力驱动
Strongly Powered by AbleSci AI