On Multi-Layered Connectionist Models: Adding Layers vs. Increasing Width
Proceedings of the Eleventh International Joint Conference on Artificial Intelligence. Parallel and Distributed Processing. Pages 176-178.
Abstract
In this paper, we explore the computational potential and limitations of the multi-layered connectionist models [Minsky and Papert, 1968]. We found that the number of layers and the width are two crucial parameters for the multilayered connectionist models. If each layer has the same size nand we increment the number of layers by 1, then the number of problems solved will increase O(nn 3 ) times. On the other hand, suppos e the number of layers is equal to 2. If we increment the width by 1, then the number of problem solved will increase O(nn 2 ) times, where n is the input size. Hence, we can extend a 2-layered connectionist model by adding layers or increasing width. Our conclusion is that increasing width is better than adding layers.
@inproceedings{ijcai1989p28,
title = {On Multi-Layered Connectionist Models: Adding Layers vs. Increasing Width},
author = {Ho, Chung-Jen},
booktitle = {Proceedings of the Eleventh International Joint Conference on
Artificial Intelligence, {IJCAI-89}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {N. S. Sridharan},
pages = {176--178},
year = {1989},
month = {8},
note = {Parallel and Distributed Processing},
url = {https://ijcai.org/proceedings/1989/28},
}
TY - CPAPER TI - On Multi-Layered Connectionist Models: Adding Layers vs. Increasing Width AU - Ho, Chung-Jen T2 - Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, IJCAI-89 PB - International Joint Conferences on Artificial Intelligence PY - 1989 SP - 176 EP - 178 UR - https://ijcai.org/proceedings/1989/28 ER -Copied
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