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» Structure Learning with Nonparametric Decomposable Models
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AAAI
2011
12 years 7 months ago
Incorporating Boosted Regression Trees into Ecological Latent Variable Models
Important ecological phenomena are often observed indirectly. Consequently, probabilistic latent variable models provide an important tool, because they can include explicit model...
Rebecca A. Hutchinson, Li-Ping Liu, Thomas G. Diet...
ICCV
2009
IEEE
15 years 25 days ago
Modelling Activity Global Temporal Dependencies using Time Delayed Probabilistic Graphical Model
We present a novel approach for detecting global behaviour anomalies in multiple disjoint cameras by learning time delayed dependencies between activities cross camera views. Sp...
Chen Change Loy, Tao Xiang and Shaogang Gong
JMLR
2010
192views more  JMLR 2010»
13 years 2 months ago
Inducing Tree-Substitution Grammars
Inducing a grammar from text has proven to be a notoriously challenging learning task despite decades of research. The primary reason for its difficulty is that in order to induce...
Trevor Cohn, Phil Blunsom, Sharon Goldwater
ICPR
2004
IEEE
14 years 9 months ago
Detection of Artificial Structures in Natural-Scene Images Using Dynamic Trees
We seek a framework that addresses localization, detection and recognition of man-made objects in natural-scene images in a unified manner. We propose to model artificial structur...
Michael C. Nechyba, Sinisa Todorovic
GECCO
2004
Springer
14 years 1 months ago
Real-Coded Bayesian Optimization Algorithm: Bringing the Strength of BOA into the Continuous World
This paper describes a continuous estimation of distribution algorithm (EDA) to solve decomposable, real-valued optimization problems quickly, accurately, and reliably. This is the...
Chang Wook Ahn, Rudrapatna S. Ramakrishna, David E...