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Author: Kazuhiro Kohara

Affiliation: Chiba Institute of Technology, Japan

Abstract: We have investigated selective learning techniques for improving the ability of back-propagation neural networks to predict large changes. We previously proposed the selective-presentation approach, in which the training data corresponding to large changes in the prediction-target time series are presented more often, and selective-learning-rate approach, in which the learning rate for training data corresponding to small changes is reduced. This paper proposes combining these two approaches to achieve fine-tuned and step-by-step selective learning of neural networks according to the degree of change. Daily stock prices are predicted as a noisy real-world problem. Combining these two approaches further improved the performance.

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Paper citation in several formats:
Kohara, K. (2008). Combining Selective-Presentation and Selective-Learning-Rate Approaches for Neural Network Forecasting of Stock Markets. In Proceedings of the 4th International Workshop on Artificial Neural Networks and Intelligent Information Processing (ICINCO 2008) - ANNIIP; ISBN 978-989-8111-33-3, SciTePress, pages 3-9. DOI: 10.5220/0001508200030009

@conference{anniip08,
author={Kazuhiro Kohara.},
title={Combining Selective-Presentation and Selective-Learning-Rate Approaches for Neural Network Forecasting of Stock Markets},
booktitle={Proceedings of the 4th International Workshop on Artificial Neural Networks and Intelligent Information Processing (ICINCO 2008) - ANNIIP},
year={2008},
pages={3-9},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001508200030009},
isbn={978-989-8111-33-3},
}

TY - CONF

JO - Proceedings of the 4th International Workshop on Artificial Neural Networks and Intelligent Information Processing (ICINCO 2008) - ANNIIP
TI - Combining Selective-Presentation and Selective-Learning-Rate Approaches for Neural Network Forecasting of Stock Markets
SN - 978-989-8111-33-3
AU - Kohara, K.
PY - 2008
SP - 3
EP - 9
DO - 10.5220/0001508200030009
PB - SciTePress

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