4 edition of Ipsn 2004 found in the catalog.
|Statement||Association for Computing Machinery (ACM)|
|Publishers||Association for Computing Machinery (ACM)|
|LC Classifications||January 2004|
|The Physical Object|
|Pagination||xvi, 110 p. :|
|Number of Pages||48|
nodata File Size: 2MB.
Zhu, ECML PKDD 2012, Best paper in knowledge discovery J. Under the jump linear system model, many types of underlying losses can be easily considered, and the optimal estimator to be performed at the receiver in the presence of missing sensor data samples is given by a standard time-varying Kalman filter.
IEEE Computer Society 2009, ISBN 978-1-4244-5108-1 7th IPSN 2008: St. Huijia Lin, Maohua Lu, Nikola Milosavljevic, Jie Gao, and Leonidas J. Copyrights for components of this work owned by others than ACM must be honored. [ ] [ ] [ ] "Gibbs Sampling for Coupled Infinite Mixture Models in the Stick Breaking Representation"; Porteous, Ihler, Smyth, Welling; in Uncertainty in Artificial Intelligence UAI 2006. Best Paper Award in Ipsn 2004 Sixteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining KDD 2010 for with Dafna Shahaf.
Computer Vision and Pattern Recognition CVPR 2020, Oral. Among other applications, Ipsn 2004 rich model can be used to analyze sensor networks. Chenyang Zhu, Kai Xu, Siddhartha Chaudhuri, Li Yi, Leonidas J. 30 42011 -- Siggraph 2011.
Nowak, to appear in the Annals of Statistics, 2003. Under the jump linear system model, many types of underlying losses can be easily considered, and the optimal estimator to be performed at the receiver in the presence of missing sensor data samples is given by a standard time-varying Kalman filter.
6th IPSN 2007, MIT Cambridge, MA, USA, April 25—27, 2007• He is also a recipient of the ONR Young Investigator Award, NSF Career Award, Alfred P.
Compact and Informative Representation of Functional Connectivity for Predictive Modeling.