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148
Voted
KDD
2009
ACM
198views Data Mining» more  KDD 2009»
16 years 3 months ago
Heterogeneous source consensus learning via decision propagation and negotiation
Nowadays, enormous amounts of data are continuously generated not only in massive scale, but also from different, sometimes conflicting, views. Therefore, it is important to conso...
Jing Gao, Wei Fan, Yizhou Sun, Jiawei Han
CIKM
2008
Springer
15 years 4 months ago
Learning the distance metric in a personal ontology
Personal ontology construction is the task of sorting through relevant materials, identifying the main topics and concepts, and organizing them to suit personal needs. Automatic c...
Hui Yang, Jamie Callan
118
Voted
IDA
2002
Springer
15 years 2 months ago
Evolutionary model selection in unsupervised learning
Feature subset selection is important not only for the insight gained from determining relevant modeling variables but also for the improved understandability, scalability, and pos...
YongSeog Kim, W. Nick Street, Filippo Menczer
142
Voted
IR
2010
15 years 1 months ago
Learning to rank with (a lot of) word features
In this article we present Supervised Semantic Indexing (SSI) which defines a class of nonlinear (quadratic) models that are discriminatively trained to directly map from the word...
Bing Bai, Jason Weston, David Grangier, Ronan Coll...
131
Voted
KDD
2004
ACM
132views Data Mining» more  KDD 2004»
16 years 3 months ago
A probabilistic framework for semi-supervised clustering
Unsupervised clustering can be significantly improved using supervision in the form of pairwise constraints, i.e., pairs of instances labeled as belonging to same or different clu...
Sugato Basu, Mikhail Bilenko, Raymond J. Mooney