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VAMOS
2009
Springer
13 years 11 months ago
Functional Variant Modeling for Adaptable Functional Networks
The application of functional networks in the automotive industry is still very slowly adopted into their development processes. Reasons for this are manifold. A functional networ...
Cem Mengi, Ibrahim Armaç
DAC
1999
ACM
14 years 5 months ago
Representation of Function Variants for Embedded System Optimization and Synthesis
Many embedded systems are implemented with a set of alternative function variants to adapt the system to different applications or environments. This paper proposes a novel approa...
Dirk Ziegenbein, Jürgen Teich, Kai Richter, L...
PAKDD
2000
ACM
161views Data Mining» more  PAKDD 2000»
13 years 8 months ago
Adaptive Boosting for Spatial Functions with Unstable Driving Attributes
Combining multiple global models (e.g. back-propagation based neural networks) is an effective technique for improving classification accuracy by reducing a variance through manipu...
Aleksandar Lazarevic, Tim Fiez, Zoran Obradovic
AAAI
2008
13 years 7 months ago
Adaptive Importance Sampling with Automatic Model Selection in Value Function Approximation
Off-policy reinforcement learning is aimed at efficiently reusing data samples gathered in the past, which is an essential problem for physically grounded AI as experiments are us...
Hirotaka Hachiya, Takayuki Akiyama, Masashi Sugiya...
BMCBI
2008
228views more  BMCBI 2008»
13 years 5 months ago
Adaptive diffusion kernel learning from biological networks for protein function prediction
Background: Machine-learning tools have gained considerable attention during the last few years for analyzing biological networks for protein function prediction. Kernel methods a...
Liang Sun, Shuiwang Ji, Jieping Ye