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IJRR
2007
186views more  IJRR 2007»
13 years 8 months ago
Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields
Learning patterns of human behavior from sensor data is extremely important for high-level activity inference. We show how to extract a person’s activities and significant plac...
Lin Liao, Dieter Fox, Henry A. Kautz
IDEAL
2010
Springer
13 years 7 months ago
Typed Linear Chain Conditional Random Fields and Their Application to Intrusion Detection
Intrusion detection in computer networks faces the problem of a large number of both false alarms and unrecognized attacks. To improve the precision of detection, various machine l...
Carsten Elfers, Mirko Horstmann, Karsten Sohr, Ott...
ACL
2010
13 years 6 months ago
Learning Better Data Representation Using Inference-Driven Metric Learning
We initiate a study comparing effectiveness of the transformed spaces learned by recently proposed supervised, and semisupervised metric learning algorithms to those generated by ...
Paramveer S. Dhillon, Partha Pratim Talukdar, Koby...
ICML
2003
IEEE
14 years 9 months ago
Link-based Classification
Over the past few years, a number of approximate inference algorithms for networked data have been put forth. We empirically compare the performance of three of the popular algori...
Qing Lu, Lise Getoor
NIPS
2008
13 years 10 months ago
Relative Performance Guarantees for Approximate Inference in Latent Dirichlet Allocation
Hierarchical probabilistic modeling of discrete data has emerged as a powerful tool for text analysis. Posterior inference in such models is intractable, and practitioners rely on...
Indraneel Mukherjee, David M. Blei