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The synergy effect's benefit is widely accepted. The object of this paper is to investigate whether a hybrid approach combining different stock prediction approaches together can dramatically outperform the single approach and compare the performance of different hybrid approaches. The hybrid model includes three well-researched algorithms: back propagation neural network (BPNN), adaptive network-based...
The stock market is considered as a high complex and dynamic system. Many machine learning and data mining technologies are used for stock analysis, but it still leaves an open question about how to integrate these methods with the plentiful knowledge and techniques accumulated in stock investment which are critical to the successful stock analysis. In this paper, we propose an intelligent stock trading...
In this paper, SVM based feature selection methods are introduced for regression problem of COX2 inhibitor activity prediction in Chinese medicine quantitative structure-activity relationship (QSAR) research. We develop a recursive SVM feature selection algorithm for regression and compare its performance with genetic algorithm and SVM recursive feature elimination (SVM-RFE) algorithm. Experiments...
In this paper, neural network based ensemble learning methods are introduced in predicting activities of COX-2 inhibitors in Chinese medicine quantitative structure-activity relationship (QSAR) research. Three different ensemble learning methods: bagging, boosting and random subspace are tested using neural networks as basic regression rules. Experiments show that all three methods, especially boosting,...
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