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» Hedging predictions in machine learning
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ML
2000
ACM
154views Machine Learning» more  ML 2000»
15 years 4 months ago
Lazy Learning of Bayesian Rules
The naive Bayesian classifier provides a simple and effective approach to classifier learning, but its attribute independence assumption is often violated in the real world. A numb...
Zijian Zheng, Geoffrey I. Webb
JCST
2011
89views more  JCST 2011»
14 years 11 months ago
Software Defect Detection with Rocus
Software defect detection aims to automatically identify defective software modules for efficient software test in order to improve the quality of a software system. Although many...
Yuan Jiang, Ming Li, Zhi-Hua Zhou
DAGM
2004
Springer
15 years 10 months ago
Learning from Labeled and Unlabeled Data Using Random Walks
We consider the general problem of learning from labeled and unlabeled data. Given a set of points, some of them are labeled, and the remaining points are unlabeled. The goal is to...
Dengyong Zhou, Bernhard Schölkopf
PKDD
2010
Springer
128views Data Mining» more  PKDD 2010»
15 years 3 months ago
Learning to Tag from Open Vocabulary Labels
Most approaches to classifying media content assume a fixed, closed vocabulary of labels. In contrast, we advocate machine learning approaches which take advantage of the millions...
Edith Law, Burr Settles, Tom M. Mitchell
JMLR
2010
121views more  JMLR 2010»
14 years 11 months ago
Sparse Semi-supervised Learning Using Conjugate Functions
In this paper, we propose a general framework for sparse semi-supervised learning, which concerns using a small portion of unlabeled data and a few labeled data to represent targe...
Shiliang Sun, John Shawe-Taylor