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» Maximum Margin Ranking Algorithms for Information Retrieval
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CIVR
2008
Springer
106views Image Analysis» more  CIVR 2008»
13 years 9 months ago
Scalable Markov model-based image annotation
In this paper, we propose a novel Markov model-based formulation for the image annotation problem. In this formulation, we treat image annotation as a graph ranking problem, by de...
Changhu Wang, Lei Zhang, Hong-Jiang Zhang
MIR
2005
ACM
140views Multimedia» more  MIR 2005»
14 years 1 months ago
Multiple random walk and its application in content-based image retrieval
In this paper, we propose a transductive learning method for content-based image retrieval: Multiple Random Walk (MRW). Its basic idea is to construct two generative models by mea...
Jingrui He, Hanghang Tong, Mingjing Li, Wei-Ying M...
KDD
2010
ACM
252views Data Mining» more  KDD 2010»
13 years 11 months ago
Fast query execution for retrieval models based on path-constrained random walks
Many recommendation and retrieval tasks can be represented as proximity queries on a labeled directed graph, with typed nodes representing documents, terms, and metadata, and labe...
Ni Lao, William W. Cohen
WWW
2008
ACM
14 years 8 months ago
Mining the search trails of surfing crowds: identifying relevant websites from user activity
The paper proposes identifying relevant information sources from the history of combined searching and browsing behavior of many Web users. While it has been previously shown that...
Mikhail Bilenko, Ryen W. White
CIKM
2008
Springer
13 years 9 months ago
Error-driven generalist+experts (edge): a multi-stage ensemble framework for text categorization
We introduce a multi-stage ensemble framework, ErrorDriven Generalist+Expert or Edge, for improved classification on large-scale text categorization problems. Edge first trains a ...
Jian Huang 0002, Omid Madani, C. Lee Giles