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104
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ICML
2010
IEEE
15 years 1 months ago
Hilbert Space Embeddings of Hidden Markov Models
Hidden Markov Models (HMMs) are important tools for modeling sequence data. However, they are restricted to discrete latent states, and are largely restricted to Gaussian and disc...
Le Song, Sajid M. Siddiqi, Geoffrey J. Gordon, Ale...
99
Voted
ICASSP
2010
IEEE
15 years 1 months ago
Robust background modeling via standard variance feature
In this paper, a novel standard variance feature is proposed for background modeling in dynamic scenes involving waving trees and ripples in water. The standard variance feature i...
Bineng Zhong, Hongxun Yao, Shaohui Liu
CORR
2008
Springer
129views Education» more  CORR 2008»
15 years 27 days ago
Tight Bounds on the Capacity of Binary Input random CDMA Systems
Abstract-- We consider code division multiple access communication over a binary input additive white Gaussian noise channel using random spreading. For a general class of symmetri...
Satish Babu Korada, Nicolas Macris
IJCV
1998
163views more  IJCV 1998»
15 years 14 days ago
CONDENSATION - Conditional Density Propagation for Visual Tracking
The problem of tracking curves in dense visual clutter is challenging. Kalman filtering is inadequate because it is based on Gaussian densities which, being unimodal, cannot repre...
Michael Isard, Andrew Blake
NCA
2007
IEEE
15 years 9 days ago
Handling of incomplete data sets using ICA and SOM in data mining
Based on independent component analysis (ICA) and self-organizing maps (SOM), this paper proposes an ISOM-DH model for the incomplete data’s handling in data mining. Under these ...
Hongyi Peng, Siming Zhu