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» Approximate Learning of Dynamic Models
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GECCO
2009
Springer
204views Optimization» more  GECCO 2009»
15 years 9 months ago
Combined structure and motion extraction from visual data using evolutionary active learning
We present a novel stereo vision modeling framework that generates approximate, yet physically-plausible representations of objects rather than creating accurate models that are c...
Krishnanand N. Kaipa, Josh C. Bongard, Andrew N. M...
IFIP
2009
Springer
15 years 11 months ago
HMM-Based Trust Model
Probabilistic trust has been adopted as an approach to taking security sensitive decisions in modern global computing environments. Existing probabilistic trust frameworks either a...
Ehab ElSalamouny, Vladimiro Sassone, Mogens Nielse...
AINA
2008
IEEE
15 years 11 months ago
Missing Value Estimation for Time Series Microarray Data Using Linear Dynamical Systems Modeling
The analysis of gene expression time series obtained from microarray experiments can be effectively exploited to understand a wide range of biological phenomena from the homeostat...
Connie Phong, Raul Singh
LCPC
2007
Springer
15 years 11 months ago
Modeling Relations between Inputs and Dynamic Behavior for General Programs
Program dynamic optimization, adaptive to runtime behavior changes, has become increasingly important for both performance and energy savings. However, most runtime optimizations o...
Xipeng Shen, Feng Mao
IDA
2009
Springer
15 years 11 months ago
Canonical Dual Approach to Binary Factor Analysis
Abstract. Binary Factor Analysis (BFA) is a typical problem of Independent Component Analysis (ICA) where the signal sources are binary. Parameter learning and model selection in B...
Ke Sun, Shikui Tu, David Yang Gao, Lei Xu