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» Learning classifiers from only positive and unlabeled data
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DAGM
2011
Springer
12 years 7 months ago
Agnostic Domain Adaptation
The supervised learning paradigm assumes in general that both training and test data are sampled from the same distribution. When this assumption is violated, we are in the setting...
Alexander Vezhnevets, Joachim M. Buhmann
JIB
2006
220views more  JIB 2006»
13 years 7 months ago
An assessment of machine and statistical learning approaches to inferring networks of protein-protein interactions
Protein-protein interactions (PPI) play a key role in many biological systems. Over the past few years, an explosion in availability of functional biological data obtained from hi...
Fiona Browne, Haiying Wang, Huiru Zheng, Francisco...
NIPS
2004
13 years 8 months ago
Confidence Intervals for the Area Under the ROC Curve
In many applications, good ranking is a highly desirable performance for a classifier. The criterion commonly used to measure the ranking quality of a classification algorithm is ...
Corinna Cortes, Mehryar Mohri
HICSS
2005
IEEE
117views Biometrics» more  HICSS 2005»
14 years 1 months ago
Movie Review Mining: a Comparison between Supervised and Unsupervised Classification Approaches
Web content mining is intended to help people discover valuable information from large amount of unstructured data on the web. Movie review mining classifies movie reviews into tw...
Pimwadee Chaovalit, Lina Zhou
SEMWEB
2010
Springer
13 years 5 months ago
Enhancing the Open-Domain Classification of Named Entity Using Linked Open Data
Many applications make use of named entity classification. Machine learning is the preferred technique adopted for many named entity classification methods where the choice of feat...
Yuan Ni, Lei Zhang, Zhaoming Qiu, Chen Wang