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» A Graphical Model for Evolutionary Optimization
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ICASSP
2011
IEEE
14 years 1 months ago
Maximum margin structure learning of Bayesian network classifiers
Recently, the margin criterion has been successfully used for parameter optimization in graphical models. We introduce maximum margin based structure learning for Bayesian network...
Franz Pernkop, Michael Wohlmay, Manfred Mücke
JPDC
2010
137views more  JPDC 2010»
14 years 8 months ago
Parallel exact inference on the Cell Broadband Engine processor
—We present the design and implementation of a parallel exact inference algorithm on the Cell Broadband Engine (Cell BE). Exact inference is a key problem in exploring probabilis...
Yinglong Xia, Viktor K. Prasanna
GECCO
2007
Springer
160views Optimization» more  GECCO 2007»
15 years 3 months ago
Hill climbing on discrete HIFF: exploring the role of DNA transposition in long-term artificial evolution
We show how a random mutation hill climber that does multilevel selection utilizes transposition to escape local optima on the discrete Hierarchical-If-And-Only-If (HIFF) problem....
Susan Khor
NC
2008
14 years 9 months ago
How crystals that sense and respond to their environments could evolve
An enduring mystery in biology is how a physical entity simple enough to have arisen spontaneously could have evolved into the complex life seen on Earth today. Cairns-Smith has pr...
Rebecca Schulman, Erik Winfree
CVPR
2008
IEEE
15 years 11 months ago
Max Margin AND/OR Graph learning for parsing the human body
We present a novel structure learning method, Max Margin AND/OR Graph (MM-AOG), for parsing the human body into parts and recovering their poses. Our method represents the human b...
Long Zhu, Yuanhao Chen, Yifei Lu, Chenxi Lin, Alan...