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Efforts have been made in financial markets to deal with price movement predicting. Recent studies have shown that the market can be outperformed by methodologies with the aid of science. In other words, it has been shown that methods based on computational intelligence can be more profitable than a buy-and-hold strategy. This paper proposes a probabilistic and dynamic chart pattern recognition hybrid...
Recently, performance of deep neural networks, especially convolutional neural networks (CNNs), has been drastically increased by elaborate network architectures, by new learning methods, and by GPU-based high-performance computation. However, there are still several difficult problems concerning back propagation, which include scheduling of learning rate and controlling locality of search (i.e.,...
This paper describes two new algorithms for optimising the structure of trained Evolving Connectionist System (ECoS) artificial neural networks (ANN). It also presents the results of preliminary empirical evaluations of the algorithms. While ECoS are fast and efficient constructive ANN algorithms they can lose efficiency if they are allowed to grow too large. The algorithms presented in this paper...
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