The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Dalam kebanyakan masalah pembelajaran mesin, adalah penting untuk menggunakan bukan sahaja data latihan, tetapi juga pengetahuan priori tentang bagaimana dunia dikekang. Dalam banyak kes, pengetahuan sedemikian diberikan dalam bentuk kekangan pada data pembezaan atau lebih khusus persamaan pembezaan separa (PDE). Rangkaian saraf dengan keupayaan untuk mempelajari data pembezaan boleh memanfaatkan pengetahuan tersebut dan dengan mudah memasukkan kekangan tersebut ke dalam pembelajaran data nilai latihan. Dalam makalah ini, kami melaporkan struktur, algoritma dan hasil eksperimen pada rangkaian saraf yang mempelajari data pembezaan.
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Salinan
Ryusuke MASUOKA, "Neural Networks Learning Differential Data" in IEICE TRANSACTIONS on Information,
vol. E83-D, no. 6, pp. 1291-1300, June 2000, doi: .
Abstract: In many of machine learning problems, it is essential to use not only the training data, but also a priori knowledge about how the world is constrained. In many cases, such knowledge is given in the forms of constraints on differential data or more specifically partial differential equations (PDEs). Neural networks with capabilities to learn differential data can take advantage of such knowledge and easily incorporate such constraints into the learning of training value data. In this paper, we report a structure, an algorithm, and results of experiments on neural networks learing differential data.
URL: https://global.ieice.org/en_transactions/information/10.1587/e83-d_6_1291/_p
Salinan
@ARTICLE{e83-d_6_1291,
author={Ryusuke MASUOKA, },
journal={IEICE TRANSACTIONS on Information},
title={Neural Networks Learning Differential Data},
year={2000},
volume={E83-D},
number={6},
pages={1291-1300},
abstract={In many of machine learning problems, it is essential to use not only the training data, but also a priori knowledge about how the world is constrained. In many cases, such knowledge is given in the forms of constraints on differential data or more specifically partial differential equations (PDEs). Neural networks with capabilities to learn differential data can take advantage of such knowledge and easily incorporate such constraints into the learning of training value data. In this paper, we report a structure, an algorithm, and results of experiments on neural networks learing differential data.},
keywords={},
doi={},
ISSN={},
month={June},}
Salinan
TY - JOUR
TI - Neural Networks Learning Differential Data
T2 - IEICE TRANSACTIONS on Information
SP - 1291
EP - 1300
AU - Ryusuke MASUOKA
PY - 2000
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E83-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2000
AB - In many of machine learning problems, it is essential to use not only the training data, but also a priori knowledge about how the world is constrained. In many cases, such knowledge is given in the forms of constraints on differential data or more specifically partial differential equations (PDEs). Neural networks with capabilities to learn differential data can take advantage of such knowledge and easily incorporate such constraints into the learning of training value data. In this paper, we report a structure, an algorithm, and results of experiments on neural networks learing differential data.
ER -