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» Causal inference using the algorithmic Markov condition
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NIPS
2007
14 years 1 days ago
Fast and Scalable Training of Semi-Supervised CRFs with Application to Activity Recognition
We present a new and efficient semi-supervised training method for parameter estimation and feature selection in conditional random fields (CRFs). In real-world applications suc...
Maryam Mahdaviani, Tanzeem Choudhury
BMCBI
2006
203views more  BMCBI 2006»
13 years 10 months ago
Genome-wide prediction of transcriptional regulatory elements of human promoters using gene expression and promoter analysis dat
Background: A complete understanding of the regulatory mechanisms of gene expression is the next important issue of genomics. Many bioinformaticians have developed methods and alg...
Seon-Young Kim, YongSung Kim
IUI
2004
ACM
14 years 4 months ago
Sheepdog: learning procedures for technical support
Technical support procedures are typically very complex. Users often have trouble following printed instructions describing how to perform these procedures, and these instructions...
Tessa A. Lau, Lawrence D. Bergman, Vittorio Castel...
CORR
2008
Springer
173views Education» more  CORR 2008»
13 years 10 months ago
Decomposition Principles and Online Learning in Cross-Layer Optimization for Delay-Sensitive Applications
In this paper, we propose a general cross-layer optimization framework in which we explicitly consider both the heterogeneous and dynamically changing characteristics of delay-sens...
Fangwen Fu, Mihaela van der Schaar
ECCV
2006
Springer
15 years 16 days ago
Tracking Dynamic Near-Regular Texture Under Occlusion and Rapid Movements
We present a dynamic near-regular texture (NRT) tracking algorithm nested in a lattice-based Markov-Random-Field (MRF) model of a 3D spatiotemporal space. One basic observation use...
Wen-Chieh Lin, Yanxi Liu