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120
Voted
KDD
2004
ACM
164views Data Mining» more  KDD 2004»
16 years 2 months ago
Cluster-based concept invention for statistical relational learning
We use clustering to derive new relations which augment database schema used in automatic generation of predictive features in statistical relational learning. Clustering improves...
Alexandrin Popescul, Lyle H. Ungar
105
Voted
WSDM
2010
ACM
242views Data Mining» more  WSDM 2010»
15 years 11 months ago
Improving Ad Relevance in Sponsored Search
We describe a machine learning approach for predicting sponsored search ad relevance. Our baseline model incorporates basic features of text overlap and we then extend the model t...
Dustin Hillard, Stefan Schroedl, Eren Manavoglu, H...
97
Voted
PKDD
2009
Springer
118views Data Mining» more  PKDD 2009»
15 years 9 months ago
The Feature Importance Ranking Measure
Most accurate predictions are typically obtained by learning machines with complex feature spaces (as e.g. induced by kernels). Unfortunately, such decision rules are hardly access...
Alexander Zien, Nicole Krämer, Sören Son...
ICDM
2008
IEEE
145views Data Mining» more  ICDM 2008»
15 years 8 months ago
Paired Learners for Concept Drift
To cope with concept drift, we paired a stable online learner with a reactive one. A stable learner predicts based on all of its experience, whereas a reactive learner predicts ba...
Stephen H. Bach, Marcus A. Maloof
ICDM
2008
IEEE
182views Data Mining» more  ICDM 2008»
15 years 8 months ago
Multiple-Instance Regression with Structured Data
We present a multiple-instance regression algorithm that models internal bag structure to identify the items most relevant to the bag labels. Multiple-instance regression (MIR) op...
Kiri L. Wagstaff, Terran Lane, Alex Roper