id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_zehnx3oazrbmhpixswqdorn3cy	Songfeng Zheng	QBoost: Predicting quantiles with boosting for regression and binary classification	2012	11	.pdf	application/pdf	8739	1041	71	algorithms which predict quantiles of the interested response for regression and binary classification. Quantile Boost Regression performs gradient descent in functional space to minimize the objective QBR, functional gradient ascent is applied to maximize the objective function, which yields the Quantile Boost Classification on this we further propose the quantile boost classification algorithm with some discussions of the related methods. Quantile boost regression (QBR) algorithm. the Quantile Boost Classification (QBC) algorithm as shown in Fig. 3. Since the purpose of this paper is to propose an alternative algorithm to the original QReg, we choose only to compare the performance of QBR to that of the original QReg. Five regression datasets resulting Quantile Boost Regression (QBR) algorithm. Quantile Boost Classification (QBC) algorithm. QBR/QBC to other quantile based regression/classification algorithms on more datasets is also desired. QBoost: Predicting quantiles with boosting for regression and binary classification QBoost: Predicting quantiles with boosting for regression and binary classification	./cache/work_zehnx3oazrbmhpixswqdorn3cy.pdf	./txt/work_zehnx3oazrbmhpixswqdorn3cy.txt