Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/17223
Title: Hybrid learning framework for web information retrieval
Authors: Feng, G
Lam, KM 
Zhang, XD
Wang, DS
Keywords: Fuzzy Set
Machine Learning
Issue Date: 2008
Publisher: IEEE
Source: 2008 International Conference on Neural Networks and Signal Processing, 7-11 June 2008, Nanjing, p. 569-574 How to cite?
Abstract: Machine learning techniques have been considered a very promising solution to Web information retrieval, which is based on the ranking of the relevance of samples to a query input. However, the connotation of labeling in ranking is quite different from that in classification. Specifically, the labeling of samples for ranking is usually incomplete, i.e. only a part of samples are labeled. In order to remedy this methodological gap, in this paper we propose a hybrid learning framework, called fuzzy-label learning, which consists of two layers. First, we utilize a label-propagation algorithm to estimate those labels of unlabeled samples by their neighborhoods. Second, we adopt RankBoost on the samples with fuzzy labels. Experiments with five-fold cross-validation using the Letor benchmark datasets show that the proposed hybrid learning framework can definitively improve the search performance achieved by the RankBoost algorithm for Web information retrieval.
URI: http://hdl.handle.net/10397/17223
ISBN: 978-1-4244-2310-1
978-1-4244-2311-8 (E-ISBN)
DOI: 10.1109/ICNNSP.2008.4590415
Appears in Collections:Conference Paper

Access
View full-text via PolyU eLinks SFX Query
Show full item record

Page view(s)

63
Last Week
3
Last month
Checked on Nov 19, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.