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BIB
2006
84views more  BIB 2006»
15 years 6 months ago
Computational methodologies for modelling, analysis and simulation of signalling networks
This article is a critical review of computational techniques used to model, analyse and simulate signalling networks. We propose a conceptual framework, and discuss the role of s...
David R. Gilbert, Hendrik Fuß, Xu Gu, Richar...
ICCV
2005
IEEE
16 years 8 months ago
Probabilistic Boosting-Tree: Learning Discriminative Models for Classification, Recognition, and Clustering
In this paper, a new learning framework?probabilistic boosting-tree (PBT), is proposed for learning two-class and multi-class discriminative models. In the learning stage, the pro...
Zhuowen Tu
ICML
2004
IEEE
16 years 7 months ago
Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data
In sequence modeling, we often wish to represent complex interaction between labels, such as when performing multiple, cascaded labeling tasks on the same sequence, or when longra...
Charles A. Sutton, Khashayar Rohanimanesh, Andrew ...
DSN
2007
IEEE
16 years 1 months ago
On a Modeling Framework for the Analysis of Interdependencies in Electric Power Systems
Nowadays, economy, security and quality of life heavily depend on the resiliency of a number of critical infrastructures, including the Electric Power System (EPS), through which ...
Silvano Chiaradonna, Paolo Lollini, Felicita Di Gi...
NIPS
2004
15 years 8 months ago
Contextual Models for Object Detection Using Boosted Random Fields
We seek to both detect and segment objects in images. To exploit both local image data as well as contextual information, we introduce Boosted Random Fields (BRFs), which use boos...
Antonio Torralba, Kevin P. Murphy, William T. Free...