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".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Este artigo propõe um algoritmo de aprendizagem de modelagem probabilística para a abordagem de busca local para redes Lógicas de Valores Múltiplos (MVL). O modelo de aprendizagem (PMLS) tem duas fases: uma fase de busca local (LS) e uma fase de modelagem probabilística (PM). O LS realiza buscas atualizando os parâmetros da rede MVL. É equivalente a uma diminuição gradiente das medidas de erro e leva a um mínimo local de erro que representa uma boa solução para o problema. Uma vez que o LS fica preso em mínimos locais, a fase PM tenta gerar um novo ponto de partida para o LS para busca adicional. Espera-se que a busca adicional seja orientada para uma área promissora pelo modelo de probabilidade. Assim, o algoritmo proposto pode escapar dos mínimos locais e buscar ainda melhores resultados. Testamos o algoritmo em muitas redes MVL geradas aleatoriamente. Os resultados da simulação mostram que o algoritmo proposto é melhor do que outros métodos aprimorados de aprendizado de busca local, como busca local dinâmica estocástica (SDLS) e busca local dinâmica caótica (CDLS).
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Shangce GAO, Qiping CAO, Masahiro ISHII, Zheng TANG, "Local Search with Probabilistic Modeling for Learning Multiple-Valued Logic Networks" in IEICE TRANSACTIONS on Fundamentals,
vol. E94-A, no. 2, pp. 795-805, February 2011, doi: 10.1587/transfun.E94.A.795.
Abstract: This paper proposes a probabilistic modeling learning algorithm for the local search approach to the Multiple-Valued Logic (MVL) networks. The learning model (PMLS) has two phases: a local search (LS) phase, and a probabilistic modeling (PM) phase. The LS performs searches by updating the parameters of the MVL network. It is equivalent to a gradient decrease of the error measures, and leads to a local minimum of error that represents a good solution to the problem. Once the LS is trapped in local minima, the PM phase attempts to generate a new starting point for LS for further search. It is expected that the further search is guided to a promising area by the probability model. Thus, the proposed algorithm can escape from local minima and further search better results. We test the algorithm on many randomly generated MVL networks. Simulation results show that the proposed algorithm is better than the other improved local search learning methods, such as stochastic dynamic local search (SDLS) and chaotic dynamic local search (CDLS).
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E94.A.795/_p
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@ARTICLE{e94-a_2_795,
author={Shangce GAO, Qiping CAO, Masahiro ISHII, Zheng TANG, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Local Search with Probabilistic Modeling for Learning Multiple-Valued Logic Networks},
year={2011},
volume={E94-A},
number={2},
pages={795-805},
abstract={This paper proposes a probabilistic modeling learning algorithm for the local search approach to the Multiple-Valued Logic (MVL) networks. The learning model (PMLS) has two phases: a local search (LS) phase, and a probabilistic modeling (PM) phase. The LS performs searches by updating the parameters of the MVL network. It is equivalent to a gradient decrease of the error measures, and leads to a local minimum of error that represents a good solution to the problem. Once the LS is trapped in local minima, the PM phase attempts to generate a new starting point for LS for further search. It is expected that the further search is guided to a promising area by the probability model. Thus, the proposed algorithm can escape from local minima and further search better results. We test the algorithm on many randomly generated MVL networks. Simulation results show that the proposed algorithm is better than the other improved local search learning methods, such as stochastic dynamic local search (SDLS) and chaotic dynamic local search (CDLS).},
keywords={},
doi={10.1587/transfun.E94.A.795},
ISSN={1745-1337},
month={February},}
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TY - JOUR
TI - Local Search with Probabilistic Modeling for Learning Multiple-Valued Logic Networks
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 795
EP - 805
AU - Shangce GAO
AU - Qiping CAO
AU - Masahiro ISHII
AU - Zheng TANG
PY - 2011
DO - 10.1587/transfun.E94.A.795
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E94-A
IS - 2
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - February 2011
AB - This paper proposes a probabilistic modeling learning algorithm for the local search approach to the Multiple-Valued Logic (MVL) networks. The learning model (PMLS) has two phases: a local search (LS) phase, and a probabilistic modeling (PM) phase. The LS performs searches by updating the parameters of the MVL network. It is equivalent to a gradient decrease of the error measures, and leads to a local minimum of error that represents a good solution to the problem. Once the LS is trapped in local minima, the PM phase attempts to generate a new starting point for LS for further search. It is expected that the further search is guided to a promising area by the probability model. Thus, the proposed algorithm can escape from local minima and further search better results. We test the algorithm on many randomly generated MVL networks. Simulation results show that the proposed algorithm is better than the other improved local search learning methods, such as stochastic dynamic local search (SDLS) and chaotic dynamic local search (CDLS).
ER -