Recurrent Neural Networks for Sequence Production
Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence. Machine Learning. Pages 1114-1118.
Abstract
In an empirical study, simple recurrent networks (SRN s, Elman, 1990) and recurrent cascade correlation networks (RCC, Fahlman, 1991) are compared with respect to a sequence production task. The application system that includes these networks extracts relevant information from incoming e-mail messages during a training period, learns the reaction to the incoming message, i.e., a sequence of user actions, and uses the learned representation for the proposal and preparation of user reactions. The performance of both network architectures in terms of network size and learning speed for a given data set is examined. Our results show that (1) RCC generates smaller networks with better performance compared to SRNs and (2) learns significantly faster than SRNs.
@inproceedings{ijcai1993p154,
title = {Recurrent Neural Networks for Sequence Production},
author = {Diederich, Joachim and Wasserschaff, Markus},
booktitle = {Proceedings of the Thirteenth International Joint Conference on
Artificial Intelligence, {IJCAI-93}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ruzena Bajcsy},
pages = {1114--1118},
year = {1993},
note = {Machine Learning},
url = {https://ijcai.org/proceedings/1993/154},
}
TY - CPAPER TI - Recurrent Neural Networks for Sequence Production AU - Diederich, Joachim AU - Wasserschaff, Markus T2 - Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, IJCAI-93 PB - International Joint Conferences on Artificial Intelligence PY - 1993 SP - 1114 EP - 1118 UR - https://ijcai.org/proceedings/1993/154 ER -Copied
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