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» Learning to Identify Unexpected Instances in the Test Set
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ACML
2009
Springer
13 years 9 months ago
Learning Algorithms for Domain Adaptation
A fundamental assumption for any machine learning task is to have training and test data instances drawn from the same distribution while having a sufficiently large number of tra...
Manas A. Pathak, Eric Nyberg
ICML
2005
IEEE
14 years 5 months ago
Supervised versus multiple instance learning: an empirical comparison
We empirically study the relationship between supervised and multiple instance (MI) learning. Algorithms to learn various concepts have been adapted to the MI representation. Howe...
Soumya Ray, Mark Craven
EMNLP
2006
13 years 6 months ago
Semantic Role Labeling via Instance-Based Learning
This paper demonstrates two methods to improve the performance of instancebased learning (IBL) algorithms for the problem of Semantic Role Labeling (SRL). Two IBL algorithms are u...
Chi-san Althon Lin, Tony C. Smith
WEBI
2010
Springer
13 years 2 months ago
Relations Expansion: Extracting Relationship Instances from the Web
In this paper, we propose a Relation Expansion framework, which uses a few seed sentences marked up with two entities to expand a set of sentences containing target relations. Duri...
Haibo Li, Yutaka Matsuo, Mitsuru Ishizuka
ICPR
2010
IEEE
13 years 2 months ago
Identifying Gender from Unaligned Facial Images by Set Classification
Abstract--Rough face alignments lead to suboptimal performance of face identification systems. In this study, we present a novel approach for identifying genders from facial images...
Wen-Sheng Chu, Chun-Rong Huang, Chu-Song Chen