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JMLR
2010
115views more  JMLR 2010»
15 years 1 months ago
Message-passing for Graph-structured Linear Programs: Proximal Methods and Rounding Schemes
The problem of computing a maximum a posteriori (MAP) configuration is a central computational challenge associated with Markov random fields. There has been some focus on “tr...
Pradeep Ravikumar, Alekh Agarwal, Martin J. Wainwr...
EMMCVPR
2011
Springer
14 years 2 months ago
Data-Driven Importance Distributions for Articulated Tracking
Abstract. We present two data-driven importance distributions for particle filterbased articulated tracking; one based on background subtraction, another on depth information. In ...
Søren Hauberg, Kim Steenstrup Pedersen
KDD
2003
ACM
191views Data Mining» more  KDD 2003»
16 years 3 months ago
Assessment and pruning of hierarchical model based clustering
The goal of clustering is to identify distinct groups in a dataset. The basic idea of model-based clustering is to approximate the data density by a mixture model, typically a mix...
Jeremy Tantrum, Alejandro Murua, Werner Stuetzle
ICCV
2007
IEEE
16 years 4 months ago
Graph Based Discriminative Learning for Robust and Efficient Object Tracking
Object tracking is viewed as a two-class 'one-versusrest' classification problem, in which the sample distribution of the target is approximately Gaussian while the back...
Xiaoqin Zhang, Weiming Hu, Stephen J. Maybank, Xi ...
TSP
2008
91views more  TSP 2008»
15 years 2 months ago
A Sequential Monte Carlo Method for Motif Discovery
We propose a sequential Monte Carlo (SMC)-based motif discovery algorithm that can efficiently detect motifs in datasets containing a large number of sequences. The statistical di...
Kuo-ching Liang, Xiaodong Wang, Dimitris Anastassi...