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ICDM
2010
IEEE
168views Data Mining» more  ICDM 2010»
15 years 2 months ago
Anomaly Detection Using an Ensemble of Feature Models
We present a new approach to semi-supervised anomaly detection. Given a set of training examples believed to come from the same distribution or class, the task is to learn a model ...
Keith Noto, Carla E. Brodley, Donna K. Slonim
SIGMOD
1996
ACM
84views Database» more  SIGMOD 1996»
15 years 8 months ago
Change Detection in Hierarchically Structured Information
Detecting and representing changes to data is important for active databases, data warehousing, view maintenance, and version and configuration management. Most previous work in c...
Sudarshan S. Chawathe, Anand Rajaraman, Hector Gar...
CIKM
2010
Springer
15 years 3 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
CVPR
2011
IEEE
14 years 8 months ago
Learning People Detection Models from Few Training Samples
People detection is an important task for a wide range of applications in computer vision. State-of-the-art methods learn appearance based models requiring tedious collection and ...
Leonid Pishchulin, Christian Wojek, Arjun Jain, Th...
WSDM
2012
ACM
245views Data Mining» more  WSDM 2012»
14 years 8 days ago
The early bird gets the buzz: detecting anomalies and emerging trends in information networks
In this work we propose a novel approach to anomaly detection in streaming communication data. We first build a stochastic model for the system based on temporal communication pa...
Brian Thompson