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» Learning to Rank with Supplementary Data
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AIRS
2010
Springer
13 years 1 months ago
Learning to Rank with Supplementary Data
This paper is concerned with a new task of ranking, referred to as "supplementary data assisted ranking", or "supplementary ranking" for short. Different from c...
Wenkui Ding, Tao Qin, Xu-Dong Zhang
AUSAI
2008
Springer
13 years 5 months ago
Learning to Find Relevant Biological Articles without Negative Training Examples
Classifiers are traditionally learned using sets of positive and negative training examples. However, often a classifier is required, but for training only an incomplete set of pos...
Keith Noto, Milton H. Saier Jr., Charles Elkan
ECML
2007
Springer
13 years 9 months ago
Learning from Relevant Tasks Only
We extend our recent work on relevant subtask learning, a new variant of multitask learning where the goal is to learn a good classifier for a task-of-interest with too few train...
Samuel Kaski, Jaakko Peltonen
SIGIR
2012
ACM
11 years 6 months ago
Top-k learning to rank: labeling, ranking and evaluation
In this paper, we propose a novel top-k learning to rank framework, which involves labeling strategy, ranking model and evaluation measure. The motivation comes from the difficul...
Shuzi Niu, Jiafeng Guo, Yanyan Lan, Xueqi Cheng
ICML
2006
IEEE
14 years 4 months ago
Ranking on graph data
In ranking, one is given examples of order relationships among objects, and the goal is to learn from these examples a real-valued ranking function that induces a ranking or order...
Shivani Agarwal