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» Learning from Multiple Sources of Inaccurate Data
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DAC
2011
ACM
12 years 7 months ago
AENEID: a generic lithography-friendly detailed router based on post-RET data learning and hotspot detection
In the era of deep sub-wavelength lithography for nanometer VLSI designs, manufacturability and yield issues are critical and need to be addressed during the key physical design i...
Duo Ding, Jhih-Rong Gao, Kun Yuan, David Z. Pan
CIKM
2009
Springer
13 years 10 months ago
Learning from past queries for resource selection
Federated text search provides a unified search interface for multiple search engines of distributed text information sources. Resource selection is an important component for fed...
Suleyman Cetintas, Luo Si, Hao Yuan
AAAI
2011
12 years 7 months ago
End-User Feature Labeling via Locally Weighted Logistic Regression
Applications that adapt to a particular end user often make inaccurate predictions during the early stages when training data is limited. Although an end user can improve the lear...
Weng-Keen Wong, Ian Oberst, Shubhomoy Das, Travis ...
PKDD
2004
Springer
97views Data Mining» more  PKDD 2004»
14 years 26 days ago
Dealing with Predictive-but-Unpredictable Attributes in Noisy Data Sources
Attribute noise can affect classification learning. Previous work in handling attribute noise has focused on those predictable attributes that can be predicted by the class and o...
Ying Yang, Xindong Wu, Xingquan Zhu
ICDM
2010
IEEE
228views Data Mining» more  ICDM 2010»
13 years 4 months ago
Active Learning from Multiple Noisy Labelers with Varied Costs
In active learning, where a learning algorithm has to purchase the labels of its training examples, it is often assumed that there is only one labeler available to label examples, ...
Yaling Zheng, Stephen D. Scott, Kun Deng