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APIN
2002
121views more  APIN 2002»
13 years 7 months ago
Applying Learning by Examples for Digital Design Automation
This paper describes a new learning by example mechanism and its application for digital circuit design automation. This mechanism uses finite state machines to represent the infer...
Ben Choi
BMCBI
2006
118views more  BMCBI 2006»
13 years 7 months ago
Predicting the effect of missense mutations on protein function: analysis with Bayesian networks
Background: A number of methods that use both protein structural and evolutionary information are available to predict the functional consequences of missense mutations. However, ...
Chris J. Needham, James R. Bradford, Andrew J. Bul...
SECON
2010
IEEE
13 years 5 months ago
Deconstructing Interference Relations in WiFi Networks
Abstract--Wireless interference is the major cause of degradation of capacity in 802.11 wireless networks. We present an approach to estimate the interference between nodes and lin...
Anand Kashyap, Utpal Paul, Samir R. Das
IJFCS
2006
130views more  IJFCS 2006»
13 years 7 months ago
Mealy multiset automata
We introduce the networks of Mealy multiset automata, and study their computational power. The networks of Mealy multiset automata are computationally complete. 1 Learning from Mo...
Gabriel Ciobanu, Viorel Mihai Gontineac
ICML
2010
IEEE
13 years 8 months ago
Continuous-Time Belief Propagation
Many temporal processes can be naturally modeled as a stochastic system that evolves continuously over time. The representation language of continuous-time Bayesian networks allow...
Tal El-Hay, Ido Cohn, Nir Friedman, Raz Kupferman