Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Conditional Random Fields for Contextual Human Motion Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Reality mining: sensing complex social systems
Personal and Ubiquitous Computing
Detecting Coarticulation in Sign Language using Conditional Random Fields
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Man-made structure detection in natural images using a causal multiscale random field
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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Mobile phone, as a kind of most commonly used vehicle of communication, keep records of every movements of each person. For each cell phone user, the different social attribute, leading to various mobility behaviors and social cliques, reflect on dissimilarity of their call behavior patterns. How to deduce the social attribute from the calling behavior is discussed in this paper, by estimated the time he spent on his business, his family or his friends. The data contains 749 users 3 months call detail records (CDR) with the 5 different jobs, which is selected randomly from database of a telecommunication operator who refuse to apprize its name. In this paper, the daily behavior of one user is divided into 48 parts with every half an hour as a basic element which is labeled with one activity-mode. There are eight activity-modes, inferred using hierarchical conditional random fields (HCRF), including four work-purpose states, two chat-purpose states and two other states as 3 basic elements of calling behavior. The cluster result is shown and the analyses of relation between the cluster and the job are made.