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» Learning Behavior Models for Hybrid Timed Systems
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KCAP
2003
ACM
15 years 9 months ago
Capturing interest through inference and visualization: ontological user profiling in recommender systems
Tools for filtering the World Wide Web exist, but they are hampered by the difficulty of capturing user preferences in such a diverse and dynamic environment. Recommender systems ...
Stuart E. Middleton, Nigel R. Shadbolt, David De R...
CVPR
2010
IEEE
16 years 9 days ago
Clustering Dynamic Textures with the Hierarchical EM Algorithm
The dynamic texture (DT) is a probabilistic generative model, defined over space and time, that represents a video as the output of a linear dynamical system (LDS). The DT model ...
Antoni Chan, Emanuele Coviello, Gert Lanckriet
HPCA
2008
IEEE
16 years 4 months ago
Automated microprocessor stressmark generation
Estimating the maximum power and thermal characteristics of a processor is essential for designing its power delivery system, packaging, cooling, and power/thermal management sche...
Ajay M. Joshi, Lieven Eeckhout, Lizy Kurian John, ...
MOBIHOC
2009
ACM
16 years 4 months ago
Aging rules: what does the past tell about the future in mobile ad-hoc networks?
The study in mobile ad-hoc networks (MANET) is facing challenges brought by recent discovery of non-exponential behavior of the inter-contact time distribution of mobile nodes. In...
Han Cai, Do Young Eun
GECCO
2007
Springer
235views Optimization» more  GECCO 2007»
15 years 10 months ago
Expensive optimization, uncertain environment: an EA-based solution
Real life optimization problems often require finding optimal solution to complex high dimensional, multimodal problems involving computationally very expensive fitness function e...
Maumita Bhattacharya