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» Generalizing over Several Learning Settings
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ICML
2004
IEEE
14 years 10 months ago
Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data
In sequence modeling, we often wish to represent complex interaction between labels, such as when performing multiple, cascaded labeling tasks on the same sequence, or when longra...
Charles A. Sutton, Khashayar Rohanimanesh, Andrew ...
KDD
2004
ACM
132views Data Mining» more  KDD 2004»
14 years 9 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
IDA
2003
Springer
14 years 2 months ago
Constructing Hierarchical Rule Systems
Rule systems have failed to attract much interest in large data analysis problems because they tend to be too simplistic to be useful or consist of too many rules for human interpr...
Thomas R. Gabriel, Michael R. Berthold
KDD
2000
ACM
121views Data Mining» more  KDD 2000»
14 years 20 days ago
Mining high-speed data streams
Many organizations today have more than very large databases; they have databases that grow without limit at a rate of several million records per day. Mining these continuous dat...
Pedro Domingos, Geoff Hulten
FLAIRS
2008
13 years 11 months ago
State Space Compression with Predictive Representations
Current studies have demonstrated that the representational power of predictive state representations (PSRs) is at least equal to the one of partially observable Markov decision p...
Abdeslam Boularias, Masoumeh T. Izadi, Brahim Chai...