Hybrid learning framework for Web information retrieval

Guang Feng, Kin Man Lam, Xu Dong Zhang, De Sheng Wang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

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.
Original languageEnglish
Title of host publication2008 IEEE International Conference Neural Networks and Signal Processing, ICNNSP
Pages569-574
Number of pages6
DOIs
Publication statusPublished - 22 Sep 2008
Event2008 IEEE International Conference Neural Networks and Signal Processing, ICNNSP - Zhenjiang, China
Duration: 7 Jun 200811 Jun 2008

Conference

Conference2008 IEEE International Conference Neural Networks and Signal Processing, ICNNSP
CountryChina
CityZhenjiang
Period7/06/0811/06/08

Keywords

  • Fuzzy set
  • Machine learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Signal Processing

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