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Author: Ryotaro Kamimura

Affiliation: Tokai University, Japan

Keyword(s): Information, entropy, information acquisition, information use, prediction performance.

Abstract: In this paper, we propose a new type of information-theoretic method to improve prediction performance in supervised learning with two main technical features. First, the complicated procedures to increase information content is replaced by the direct use of hidden neuron outputs. We realize higher information by directly changing the outputs from hidden neurons. In addition, we have had difficulty in increasing information content and at the same time decreasing errors between targets and outputs. To cope with this problem, we separate information acquisition and use phase learning. In the information acquisition phase, the auto-encoder tries to acquire information content on input patterns as much as possible. In the information use phase, information obtained in the phase of information acquisition is used to train supervised learning. The method is a simplified version of actual information maximization and directly deals with the outputs from neurons. We applied the method to t he protein classification problem. Experimental results showed that our simplified information acquisition method was effective in increasing the real information content. In addition, by using the information content, prediction performance was greatly improved. (More)

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Paper citation in several formats:
Kamimura, R. (2014). Simplified Information Acquisition Method to Improve Prediction Performance: Direct Use of Hidden Neuron Outputs and Separation of Information Acquisition and Use Phase. In Proceedings of the International Workshop on Artificial Neural Networks and Intelligent Information Processing (ICINCO 2014) - ANNIIP; ISBN 978-989-758-041-3, SciTePress, pages 78-87. DOI: 10.5220/0005134700780087

@conference{anniip14,
author={Ryotaro Kamimura.},
title={Simplified Information Acquisition Method to Improve Prediction Performance: Direct Use of Hidden Neuron Outputs and Separation of Information Acquisition and Use Phase},
booktitle={Proceedings of the International Workshop on Artificial Neural Networks and Intelligent Information Processing (ICINCO 2014) - ANNIIP},
year={2014},
pages={78-87},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005134700780087},
isbn={978-989-758-041-3},
}

TY - CONF

JO - Proceedings of the International Workshop on Artificial Neural Networks and Intelligent Information Processing (ICINCO 2014) - ANNIIP
TI - Simplified Information Acquisition Method to Improve Prediction Performance: Direct Use of Hidden Neuron Outputs and Separation of Information Acquisition and Use Phase
SN - 978-989-758-041-3
AU - Kamimura, R.
PY - 2014
SP - 78
EP - 87
DO - 10.5220/0005134700780087
PB - SciTePress

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