id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-330596-p4k7jexz	Hu, Ji	An integrated classification model for incremental learning	2020-10-21		.txt	text/plain	4518	252	51	However, existing incremental learning methods face two significant problems: 1) noise in the classification sample data, 2) poor accuracy of modern classification algorithms when applied to modern classification problems. In order to deal with these issues, this paper proposes an integrated classification model, known as a Pre-trained Truncated Gradient Confidence-weighted (Pt-TGCW) model. This method consists of two parts: a pre-trained (Pt) model and a novel Truncated Gradient Confidence-weighted online classification model (TGCW). Online learning is a continuous training process in which input values are fed into the model in each round of training, and the model outputs prediction results based on the current parameters [16] . In this section, we propose a new online learning algorithm suitable for binary classification of streamed data, named TGCW, which aims to further improve the prediction accuracy and feature selection capability of the model. In addition, we will also look for improved pre-trained models or use more classifiers for integrated learning to improve the classification accuracy of complex data.	./cache/cord-330596-p4k7jexz.txt	./txt/cord-330596-p4k7jexz.txt