Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
C4.5: programs for machine learning
C4.5: programs for machine learning
Growing artificial societies: social science from the bottom up
Growing artificial societies: social science from the bottom up
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
An Adaptive Agent Based Economic Model
Learning Classifier Systems, From Foundations to Applications
Agent Mining: The Synergy of Agents and Data Mining
IEEE Intelligent Systems
Analyzing the influence of overconfident investors on financial markets through agent-based model
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
ACMOS'10 Proceedings of the 12th WSEAS international conference on Automatic control, modelling & simulation
Learning and predicting financial time series by combining natural computation and agent simulation
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part II
Computational learning techniques for intraday FX trading using popular technical indicators
IEEE Transactions on Neural Networks
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Integrating agent based modeling with learning results in a promising methodology to model the behavior of financial markets. We discuss here how partial and full knowledge learning setups can be combined with agent based modeling to approximate the behavior of financial time series. Partial knowledge learners operate with limited knowledge of the domain, usually only the initial conditions are used. While full knowledge learners use any domain data any time it is made available to adjust their predictions. We report in this paper an experimental study of our learning system L-FABS, introduced in previous works, in order to show how it can discover models for approximating time series working in partial knowledge and full knowledge learning scenarios.