@inproceedings{7da4659dc9284e80b875eff1732406f3,
title = "A Method for Topic Classification of Web Pages Using LDA-SVM Model",
abstract = "The fast developments on the computer and networking technologies have made the Internet become the largest medium of information in the word at present. Many companies hope to be able to timely and effective access to information from the Internet. Efficient webpages classification system is needed. According to the classification requirements, we use LDA-SVM model for elaborate web category classification. And we discuss the impact of topic number K in LDA to the classification. The experiments show our method is efficient.",
keywords = "LDA, SVM, Webpages classification",
author = "Yuliang Wei and Wei Wang and Bailing Wang and Bo Yang and Yang Liu",
note = "Publisher Copyright: {\textcopyright} 2018, Springer Nature Singapore Pte Ltd.; Chinese Intelligent Automation Conference, CIAC 2017 ; Conference date: 02-06-2017 Through 04-06-2017",
year = "2018",
doi = "10.1007/978-981-10-6445-6\_64",
language = "英语",
isbn = "9789811064449",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Verlag",
pages = "589--596",
editor = "Zhidong Deng",
booktitle = "Proceedings of 2017 Chinese Intelligent Automation Conference",
address = "德国",
}