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» Improving distant supervision using inference learning
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ICANN
2011
Springer
13 years 1 months ago
Bias of Importance Measures for Multi-valued Attributes and Solutions
Attribute importance measures for supervised learning are important for improving both learning accuracy and interpretability. However, it is well-known there could be bias when th...
Houtao Deng, George C. Runger, Eugene Tuv
ICML
2007
IEEE
14 years 10 months ago
Asymptotic Bayesian generalization error when training and test distributions are different
In supervised learning, we commonly assume that training and test data are sampled from the same distribution. However, this assumption can be violated in practice and then standa...
Keisuke Yamazaki, Klaus-Robert Müller, Masash...
ECML
2004
Springer
14 years 3 months ago
Exploiting Unlabeled Data in Content-Based Image Retrieval
Abstract. In this paper, the Ssair (Semi-Supervised Active Image Retrieval) approach, which attempts to exploit unlabeled data to improve the performance of content-based image ret...
Zhi-Hua Zhou, Ke-Jia Chen, Yuan Jiang
CVPR
2003
IEEE
14 years 12 months ago
Learning Bayesian Network Classifiers for Facial Expression Recognition using both Labeled and Unlabeled Data
Understanding human emotions is one of the necessary skills for the computer to interact intelligently with human users. The most expressive way humans display emotions is through...
Ira Cohen, Nicu Sebe, Fabio Gagliardi Cozman, Marc...
AAAI
2008
14 years 7 days ago
Structure Learning on Large Scale Common Sense Statistical Models of Human State
Research has shown promise in the design of large scale common sense probabilistic models to infer human state from environmental sensor data. These models have made use of mined ...
William Pentney, Matthai Philipose, Jeff A. Bilmes