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» Using Markov Blankets for Causal Structure Learning
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BMCBI
2006
119views more  BMCBI 2006»
15 years 3 months ago
Hidden Markov Model Variants and their Application
Markov statistical methods may make it possible to develop an unsupervised learning process that can automatically identify genomic structure in prokaryotes in a comprehensive way...
Stephen Winters-Hilt
126
Voted
ICASSP
2010
IEEE
15 years 3 months ago
Learning deep rhetorical structure for extractive speech summarization
Extractive summarization of conference and lecture speech is useful for online learning and references. We show for the first time that deep(er) rhetorical parsing of conference ...
Justin Jian Zhang, Pascale Fung
192
Voted
ICMCS
2009
IEEE
415views Multimedia» more  ICMCS 2009»
15 years 1 months ago
A new localized superpixel Markov random field for image segmentation
In this paper, we present a novel localized Markov random field (MRF) method based on superpixels for region segmentation. Early vision problems could be formulated as pixel label...
Xiaofeng Wang, Xiao-Ping Zhang
128
Voted
IDA
2009
Springer
15 years 10 months ago
Estimating Markov Random Field Potentials for Natural Images
Markov Random Field (MRF) models with potentials learned from the data have recently received attention for learning the low-level structure of natural images. A MRF provides a pri...
Urs Köster, Jussi T. Lindgren, Aapo Hyvä...
123
Voted
ECML
2007
Springer
15 years 9 months ago
Spectral Clustering and Embedding with Hidden Markov Models
Abstract. Clustering has recently enjoyed progress via spectral methods which group data using only pairwise affinities and avoid parametric assumptions. While spectral clustering ...
Tony Jebara, Yingbo Song, Kapil Thadani