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» Reformulating Constraint Models Using Input Data
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GECCO
2003
Springer
139views Optimization» more  GECCO 2003»
15 years 10 months ago
Daily Stock Prediction Using Neuro-genetic Hybrids
We propose a neuro-genetic daily stock prediction model. Traditional indicators of stock prediction are utilized to produce useful input features of neural networks. The genetic al...
Yung-Keun Kwon, Byung Ro Moon
143
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COOPIS
2004
IEEE
15 years 8 months ago
Learning Classifiers from Semantically Heterogeneous Data
Semantically heterogeneous and distributed data sources are quite common in several application domains such as bioinformatics and security informatics. In such a setting, each dat...
Doina Caragea, Jyotishman Pathak, Vasant Honavar
FAST
2009
15 years 2 months ago
A Formal Model of Provenance in Distributed Systems
We present a formalism for provenance in distributed systems based on the -calculus. Its main feature is that all data products are annotated with metadata representing their prov...
Issam Souilah, Adrian Francalanza, Vladimiro Sasso...
ICIAP
2003
ACM
16 years 4 months ago
Multimodal biometric authentication using quality signals in mobile communications
The elements of multimodal authentication along with system models are presented. These include the machine experts as well as machine supervisors. In particular fingerprint and s...
Josef Bigün, Julian Fiérrez-Aguilar, J...
WSC
1998
15 years 6 months ago
Integrating Neural Networks with Special Purpose Simulation
Traditional methods of dealing with variability in simulation input data are mainly stochastic. This is most often the best method to use if the factors affecting the variation or...
Dany Hajjar, Simaan M. AbouRizk, Kevin Mather