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KDD
2002
ACM
157views Data Mining» more  KDD 2002»
15 years 10 months ago
Exploiting unlabeled data in ensemble methods
An adaptive semi-supervised ensemble method, ASSEMBLE, is proposed that constructs classification ensembles based on both labeled and unlabeled data. ASSEMBLE alternates between a...
Kristin P. Bennett, Ayhan Demiriz, Richard Maclin
KDD
2006
ACM
180views Data Mining» more  KDD 2006»
15 years 10 months ago
Learning the unified kernel machines for classification
Kernel machines have been shown as the state-of-the-art learning techniques for classification. In this paper, we propose a novel general framework of learning the Unified Kernel ...
Steven C. H. Hoi, Michael R. Lyu, Edward Y. Chang
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ICML
2007
IEEE
15 years 10 months ago
On the relation between multi-instance learning and semi-supervised learning
Multi-instance learning and semi-supervised learning are different branches of machine learning. The former attempts to learn from a training set consists of labeled bags each con...
Zhi-Hua Zhou, Jun-Ming Xu
AAAI
2012
12 years 12 months ago
Towards Discovering What Patterns Trigger What Labels
In many real applications, especially those involving data objects with complicated semantics, it is generally desirable to discover the relation between patterns in the input spa...
Yu-Feng Li, Ju-Hua Hu, Yuang Jiang, Zhi-Hua Zhou
LREC
2008
128views Education» more  LREC 2008»
14 years 11 months ago
Relation between Agreement Measures on Human Labeling and Machine Learning Performance: Results from an Art History Domain
We discuss factors that affect human agreement on a semantic labeling task in the art history domain, based on the results of four experiments where we varied the number of labels...
Rebecca J. Passonneau, Thomas Lippincott, Tae Yano...