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KDD
2009
ACM
207views Data Mining» more  KDD 2009»
14 years 9 months ago
DynaMMo: mining and summarization of coevolving sequences with missing values
Given multiple time sequences with missing values, we propose DynaMMo which summarizes, compresses, and finds latent variables. The idea is to discover hidden variables and learn ...
Lei Li, James McCann, Nancy S. Pollard, Christos F...
PERCOM
2004
ACM
14 years 8 months ago
PrudentExposure: A Private and User-centric Service Discovery Protocol
: Service Discovery as an essential element in pervasive computing environments is widely accepted. Much active research on service discovery has been conducted, but privacy has be...
Feng Zhu, Matt W. Mutka, Lionel M. Ni
CVPR
2010
IEEE
14 years 4 months ago
Automatic Discovery of Meaningful Object Parts with Latent CRFs
Object recognition is challenging due to high intra-class variability caused, e.g., by articulation, viewpoint changes, and partial occlusion. Successful methods need to strike a...
Paul Schnitzspan, Stefan Roth, Bernt Schiele
SAC
2010
ACM
14 years 3 months ago
Hypothesis generation and ranking based on event similarities
Accelerated by the technological advances in the domain, the size of the biomedical literature has been growing rapidly. As a result, it is not feasible for individual researchers...
Taiki Miyanishi, Kazuhiro Seki, Kuniaki Uehara
CSE
2009
IEEE
14 years 3 months ago
Collaborative Mining in Multiple Social Networks Data for Criminal Group Discovery
—The hidden knowledge in social networks data can be regarded as an important resource for criminal investigations which can help finding the structure and organization of a crim...
Amin Milani Fard, Martin Ester