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GECCO
2003
Springer
139views Optimization» more  GECCO 2003»
14 years 2 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
BMCBI
2007
197views more  BMCBI 2007»
13 years 9 months ago
Boolean networks using the chi-square test for inferring large-scale gene regulatory networks
Background: Boolean network (BN) modeling is a commonly used method for constructing gene regulatory networks from time series microarray data. However, its major drawback is that...
Haseong Kim, Jae K. Lee, Taesung Park
TSD
2010
Springer
13 years 6 months ago
A Priori and A Posteriori Machine Learning and Nonlinear Artificial Neural Networks
The main idea of a priori machine learning is to apply a machine learning method on a machine learning problem itself. We call it "a priori" because the processed data se...
Jan Zelinka, Jan Romportl, Ludek Müller
QSIC
2008
IEEE
14 years 3 months ago
Using Machine Learning to Refine Black-Box Test Specifications and Test Suites
In the context of open source development or software evolution, developers are often faced with test suites which have been developed with no apparent rationale and which may nee...
Lionel C. Briand, Yvan Labiche, Zaheer Bawar
NN
2007
Springer
13 years 8 months ago
Environmentally adaptive acoustic transmission loss prediction in turbulent and nonturbulent atmospheres
An environmentally adaptive system for prediction of acoustic transmission loss (TL) in the atmosphere is developed in this paper. This system uses several back propagation neural...
Gordon Wichern, Mahmood R. Azimi-Sadjadi, Michael ...