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WWW
2011
ACM
15 years 11 days ago
Learning to rank with multiple objective functions
We investigate the problem of learning to rank for document retrieval from the perspective of learning with multiple objective functions. We present solutions to two open problems...
Krysta Marie Svore, Maksims Volkovs, Christopher J...
ASUNAM
2010
IEEE
15 years 7 months ago
Semi-Supervised Classification of Network Data Using Very Few Labels
The goal of semi-supervised learning (SSL) methods is to reduce the amount of labeled training data required by learning from both labeled and unlabeled instances. Macskassy and Pr...
Frank Lin, William W. Cohen
SDM
2011
SIAM
233views Data Mining» more  SDM 2011»
14 years 8 months ago
Multi-Instance Mixture Models
Multi-instance (MI) learning is a variant of supervised learning where labeled examples consist of bags (i.e. multi-sets) of feature vectors instead of just a single feature vecto...
James R. Foulds, Padhraic Smyth
CORR
2002
Springer
117views Education» more  CORR 2002»
15 years 5 months ago
Contextual Normalization Applied to Aircraft Gas Turbine Engine Diagnosis
Diagnosing faults in aircraft gas turbine engines is a complex problem. It involves several tasks, including rapid and accurate interpretation of patterns in engine sensor data. W...
Peter D. Turney, Michael Halasz
ICDM
2008
IEEE
190views Data Mining» more  ICDM 2008»
15 years 12 months ago
Simultaneous Co-segmentation and Predictive Modeling for Large, Temporal Marketing Data
Several marketing problems involve prediction of customer purchase behavior and forecasting future preferences. We consider predictive modeling of large scale, bi-modal or multimo...
Meghana Deodhar, Joydeep Ghosh