The nature of statistical learning theory
The nature of statistical learning theory
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Optimal linear combinations of neural networks
Neural Networks
Making large-scale support vector machine learning practical
Advances in kernel methods
Hybrid Intelligent Systems for Stock Market Analysis
ICCS '01 Proceedings of the International Conference on Computational Science-Part II
Neuro Fuzzy Systems: Sate-of-the-Art Modeling Techniques
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Performance analysis of connectionist paradigms for modeling chaotic behavior of stock indices
Second international workshop on Intelligent systems design and application
Modeling chaotic behavior of stock indices using intelligent paradigms
Neural, Parallel & Scientific Computations - Special issue: Advances in intelligent systems and applications
Boosting the differences: A fast Bayesian classifier neural network
Intelligent Data Analysis
Weather analysis using ensemble of connectionist learning paradigms
Applied Soft Computing
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
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The use of intelligent systems for stock market predictions has been widely established. In this paper, we investigate how the seemingly chaotic behavior of stock markets could be well-represented using ensemble of intelligent paradigms. To demonstrate the proposed technique, we considered Nasdaq-100 index of Nasdaq Stock MarketSMand the Samp;P CNX NIFTY stock index. The intelligent paradigms considered were an artificial neural network trained using Levenberg-Marquardt algorithm, support vector machine, Ta- kagi-Sugeno neuro-fuzzy model and a difference boosting neural network. The different paradigms were combined using two different ensemble approaches so as to optimize the performance by reducing the different error measures. The first approach is based on a direct error measure and the second method is based on an evolutionary algorithm to search the optimal linear combination of the different intelligent paradigms. Experimental results reveal that the ensemble techniques performed better than the individual methods and the direct ensemble approach seems to work well for the problem considered.