Solving analogical equations between strings of symbols using neural networks

Vivatchai Kaveeta, Yves Lepage

研究成果: Conference article査読

5 被引用数 (Scopus)

抄録

A neural network model to solve analogical equations between strings of symbols is proposed. The method transforms the input strings into two fixed size alignment matrices. The matrices act as the input of the neural network which predicts two output matrices. Finally, a string decoder transforms the predicted matrices into the final string output. By design, the neural network is constrained by several properties of analogy. The experimental results show a fast learning rate with a high prediction accuracy that can beat a baseline algorithm.

本文言語English
ページ(範囲)67-76
ページ数10
ジャーナルCEUR Workshop Proceedings
1815
出版ステータスPublished - 2016
イベント24th International Conference on Case-Based Reasoning Workshops, ICCBR-WS 2016 - Atlanta, United States
継続期間: 2016 10 312016 11 2

ASJC Scopus subject areas

  • コンピュータ サイエンス(全般)

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