Boltzmann Machines with Identified States
Summary :
Learning for boltzmann machines deals with each state individually. If given data is categorized, the probabilities have to be distributed to each state, not to each catetory. We propose boltzmann machines identifying the states in the same categories. Boltzmann machines with hidden units are the special cases. Boltzmann learning and em algorithm are effective learning methods for boltzmann machines. We solve boltzmann learning and em algorithm for the proposed models.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E91-A No.3 pp.887-890
- Publication Date
- 2008/03/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1093/ietfec/e91-a.3.887
- Type of Manuscript
- LETTER
- Category
- Nonlinear Problems
Authors
Keyword
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Masaki KOBAYASHI, "Boltzmann Machines with Identified States" in IEICE TRANSACTIONS on Fundamentals,
vol. E91-A, no. 3, pp. 887-890, March 2008, doi: 10.1093/ietfec/e91-a.3.887.
Abstract: Learning for boltzmann machines deals with each state individually. If given data is categorized, the probabilities have to be distributed to each state, not to each catetory. We propose boltzmann machines identifying the states in the same categories. Boltzmann machines with hidden units are the special cases. Boltzmann learning and em algorithm are effective learning methods for boltzmann machines. We solve boltzmann learning and em algorithm for the proposed models.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e91-a.3.887/_p
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@ARTICLE{e91-a_3_887,
author={Masaki KOBAYASHI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Boltzmann Machines with Identified States},
year={2008},
volume={E91-A},
number={3},
pages={887-890},
abstract={Learning for boltzmann machines deals with each state individually. If given data is categorized, the probabilities have to be distributed to each state, not to each catetory. We propose boltzmann machines identifying the states in the same categories. Boltzmann machines with hidden units are the special cases. Boltzmann learning and em algorithm are effective learning methods for boltzmann machines. We solve boltzmann learning and em algorithm for the proposed models.},
keywords={},
doi={10.1093/ietfec/e91-a.3.887},
ISSN={1745-1337},
month={March},}
Copy
TY - JOUR
TI - Boltzmann Machines with Identified States
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 887
EP - 890
AU - Masaki KOBAYASHI
PY - 2008
DO - 10.1093/ietfec/e91-a.3.887
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E91-A
IS - 3
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - March 2008
AB - Learning for boltzmann machines deals with each state individually. If given data is categorized, the probabilities have to be distributed to each state, not to each catetory. We propose boltzmann machines identifying the states in the same categories. Boltzmann machines with hidden units are the special cases. Boltzmann learning and em algorithm are effective learning methods for boltzmann machines. We solve boltzmann learning and em algorithm for the proposed models.
ER -
English
