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» Learning to Identify Unexpected Instances in the Test Set
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IJCAI
2007
9 years 8 months ago
Learning to Identify Unexpected Instances in the Test Set
Traditional classification involves building a classifier using labeled training examples from a set of predefined classes and then applying the classifier to classify test instan...
Xiaoli Li, Bing Liu, See-Kiong Ng
PASTE
2010
ACM
9 years 12 months ago
Expect the unexpected: error code mismatches between documentation and the real world
Inaccurate documentation can mislead programmers and cause software to fail in unexpected ways. We examine mismatches between documented and actual error codes returned by 42 Linu...
Cindy Rubio-González, Ben Liblit
ICML
2003
IEEE
10 years 7 months ago
Identifying Predictive Structures in Relational Data Using Multiple Instance Learning
This paper introduces an approach for identifying predictive structures in relational data using the multiple-instance framework. By a predictive structure, we mean a structure th...
Amy McGovern, David Jensen
CVPR
2007
IEEE
10 years 8 months ago
Multiple Instance Learning of Pulmonary Embolism Detection with Geodesic Distance along Vascular Structure
We propose a novel classification approach for automatically detecting pulmonary embolism (PE) from computedtomography-angiography images. Unlike most existing approaches that req...
Jinbo Bi, Jianming Liang
AUSAI
2008
Springer
9 years 8 months ago
Revisiting Multiple-Instance Learning Via Embedded Instance Selection
Multiple-Instance Learning via Embedded Instance Selection (MILES) is a recently proposed multiple-instance (MI) classification algorithm that applies a single-instance base learne...
James R. Foulds, Eibe Frank
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