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» Learning Classifiers from Semantically Heterogeneous Data
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ML
2010
ACM
135views Machine Learning» more  ML 2010»
13 years 3 months ago
Multi-domain learning by confidence-weighted parameter combination
State-of-the-art statistical NLP systems for a variety of tasks learn from labeled training data that is often domain specific. However, there may be multiple domains or sources o...
Mark Dredze, Alex Kulesza, Koby Crammer
TREC
2004
13 years 10 months ago
Feature Generation, Feature Selection, Classifiers, and Conceptual Drift for Biomedical Document Triage
We approached the problem of classifying papers for the TREC 2004 Genomics Track triage task as a four step process: feature generation, feature selection, classifier training, an...
Aaron M. Cohen, Ravi Teja Bhupatiraju, William R. ...
KDD
2009
ACM
142views Data Mining» more  KDD 2009»
14 years 9 months ago
Quantification and semi-supervised classification methods for handling changes in class distribution
In realistic settings the prevalence of a class may change after a classifier is induced and this will degrade the performance of the classifier. Further complicating this scenari...
Jack Chongjie Xue, Gary M. Weiss
NIPS
2008
13 years 10 months ago
Exact Convex Confidence-Weighted Learning
Confidence-weighted (CW) learning [6], an online learning method for linear classifiers, maintains a Gaussian distributions over weight vectors, with a covariance matrix that repr...
Koby Crammer, Mark Dredze, Fernando Pereira
SDM
2012
SIAM
282views Data Mining» more  SDM 2012»
11 years 11 months ago
Citation Prediction in Heterogeneous Bibliographic Networks
To reveal information hiding in link space of bibliographical networks, link analysis has been studied from different perspectives in recent years. In this paper, we address a no...
Xiao Yu, Quanquan Gu, Mianwei Zhou, Jiawei Han