2100 search results - page 131 / 420 » Learning to rank on graphs |
179
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MLG
2007
Springer
16 years 17 days ago
2007
Springer
Recent investigations [12, 2, 8, 5, 6] and [11, 9] indicate the use of a probabilistic (’learning’) perspective of tasks defined on a single graph, as opposed to the traditio...
156
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ECML
2006
Springer
15 years 10 months ago
2006
Springer
In many graph-based semi-supervised learning algorithms, edge weights are assumed to be fixed and determined by the data points' (often symmetric) relationships in input space...
168
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ICML
2007
IEEE
16 years 7 months ago
2007
IEEE
Given a large, real graph, how can we generate a synthetic graph that matches its properties, i.e., it has similar degree distribution, similar (small) diameter, similar spectrum,...
152
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ECIR
2009
Springer
16 years 3 months ago
2009
Springer
Ranking for multilingual information retrieval (MLIR) is a task to rank documents of different languages solely based on their relevancy to the query regardless of query’s langu...
188
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CIKM
2009
Springer
15 years 11 months ago
2009
Springer
Traditional ranking mainly focuses on one type of data source, and effective modeling still relies on a sufficiently large number of labeled or supervised examples. However, in m...