@inproceedings{96f50ec857824b728f37d33b892a13ef,
title = "Design for fast adaboost with feature selection",
abstract = "Since the original Adaboost algorithm is very time-consuming in training, we have designed an improved Adaboost, which adds a process of feature selection to the original Adaboost algorithm. After each round of training, we retain the features whose error rate to classify the samples are relatively low and remove the features with high error rate. In this way the time for the next training is reduced, and the whole algorithm is accelerated.",
keywords = "Adaboost, Feature selection, Machine learning",
author = "Liang, {Xiao Long} and Li, {Wei Hua} and Xin Lin",
year = "2013",
doi = "10.4028/www.scientific.net/AMR.816-817.566",
language = "英语",
isbn = "9783037858677",
series = "Advanced Materials Research",
pages = "566--569",
booktitle = "Manufacturing Science and Technology (ICMST2013)",
note = "4th International Conference on Manufacturing Science and Technology, ICMST 2013 ; Conference date: 03-08-2013 Through 04-08-2013",
}