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» Learning Rule Representations from Boolean Data
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JMLR
2002
89views more  JMLR 2002»
15 years 6 days ago
The Set Covering Machine
We extend the classical algorithms of Valiant and Haussler for learning compact conjunctions and disjunctions of Boolean attributes to allow features that are constructed from the...
Mario Marchand, John Shawe-Taylor
99
Voted
KDD
1997
ACM
96views Data Mining» more  KDD 1997»
15 years 4 months ago
Using General Impressions to Analyze Discovered Classification Rules
One of the important problems in data mining is the evaluation of subjective interestingness of the discovered rules. Past research has found that in many real-life applications i...
Bing Liu, Wynne Hsu, Shu Chen
ICML
2004
IEEE
16 years 1 months ago
Kernel conditional random fields: representation and clique selection
Kernel conditional random fields (KCRFs) are introduced as a framework for discriminative modeling of graph-structured data. A representer theorem for conditional graphical models...
John D. Lafferty, Xiaojin Zhu, Yan Liu
101
Voted
CIKM
2005
Springer
15 years 6 months ago
Information retrieval and machine learning for probabilistic schema matching
Schema matching is the problem of finding correspondences (mapping rules, e.g. logical formulae) between heterogeneous schemas e.g. in the data exchange domain, or for distribute...
Henrik Nottelmann, Umberto Straccia
106
Voted
CIKM
2010
Springer
14 years 11 months ago
Partial drift detection using a rule induction framework
The major challenge in mining data streams is the issue of concept drift, the tendency of the underlying data generation process to change over time. In this paper, we propose a g...
Damon Sotoudeh, Aijun An