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» Learning from Ambiguously Labeled Examples
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CVPR
2011
IEEE
12 years 9 months ago
Multi-label Learning with Incomplete Class Assignments
We consider a special type of multi-label learning where class assignments of training examples are incomplete. As an example, an instance whose true class assignment is (c1, c2, ...
Serhat Bucak, Rong Jin, Anil Jain
ML
2008
ACM
150views Machine Learning» more  ML 2008»
13 years 5 months ago
Learning probabilistic logic models from probabilistic examples
Abstract. We revisit an application developed originally using Inductive Logic Programming (ILP) by replacing the underlying Logic Program (LP) description with Stochastic Logic Pr...
Jianzhong Chen, Stephen Muggleton, José Car...
WWW
2007
ACM
14 years 6 months ago
Identifying ambiguous queries in web search
It is widely believed that some queries submitted to search engines are by nature ambiguous (e.g., java, apple). However, few studies have investigated the questions of "how ...
Ruihua Song, Zhenxiao Luo, Ji-Rong Wen, Yong Yu, H...
ICANN
2009
Springer
13 years 10 months ago
Learning SVMs from Sloppily Labeled Data
This paper proposes a modelling of Support Vector Machine (SVM) learning to address the problem of learning with sloppy labels. In binary classification, learning with sloppy labe...
Guillaume Stempfel, Liva Ralaivola
AI
2007
Springer
13 years 6 months ago
Learning action models from plan examples using weighted MAX-SAT
AI planning requires the definition of action models using a formal action and plan description language, such as the standard Planning Domain Definition Language (PDDL), as inp...
Qiang Yang, Kangheng Wu, Yunfei Jiang