id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_tx2wszrrlzbyhcn3vjiblxod4y	Yukimasa Kaneda	Sliding window-based support vector regression for predicting micrometeorological data	2016	9	.pdf	application/pdf	9445	1703	69	Therefore, it is difficult to predict micrometeorological data accurately with low computational complexity even if state-of-the-art machine learning easily, SW-SVR builds several SVRs specialized for each representative data group in various natural environments, such as different seasons and climates, and changes weights to aggregate the SVRs dynamically To predict micrometeorological data effectively, a number of reearchers have studied machine learning ( Smith, Hoogenboom, & non-linear relationships, SVMs have also been applied to micrometeorological data prediction ( Antonanzas, Urraca, Martinezde-Pison, & Antonanzas-Torres, 2015; Mohammadi, Shamshirband, We propose a new methodology for predicting micrometeorological data, sliding window-based support vector regression, combining methodologies of SVR and ensemble learning. SVRs are built based on D-SDC that extracts effective data for specific data prediction by taking account of movements: changes of Sliding window-based support vector regression for predicting micrometeorological data Sliding window-based support vector regression for predicting micrometeorological data	./cache/work_tx2wszrrlzbyhcn3vjiblxod4y.pdf	./txt/work_tx2wszrrlzbyhcn3vjiblxod4y.txt