Zobrazeno 1 - 10 of 77 pro vyhledávání: '"Muneki Yasuda"'
1
Akademický článek
Learning Algorithm of Boltzmann Machine Based on Spatial Monte Carlo Integration Method
Autor: Muneki Yasuda
Publikováno v: Algorithms, Vol 11, Iss 4, p 42 (2018)
The machine learning techniques for Markov random fields are fundamental in various fields involving pattern recognition, image processing, sparse modeling, and earth science, and a Boltzmann machine is one of the most important models in Markov rand
Externí odkaz: https://doaj.org/article/42f867a11e2e474caf56630f07c0fe89
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2
Akademický článek
Belief Propagation Algorithm for Portfolio Optimization Problems.
Autor: Takashi Shinzato, Muneki Yasuda
Publikováno v: PLoS ONE, Vol 10, Iss 8, p e0134968 (2015)
The typical behavior of optimal solutions to portfolio optimization problems with absolute deviation and expected shortfall models using replica analysis was pioneeringly estimated by S. Ciliberti et al. [Eur. Phys. B. 57, 175 (2007)]; however, they
Externí odkaz: https://doaj.org/article/5fa63d4ef7f94702b279caa69c21f09c
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6
A Generalization of Spatial Monte Carlo Integration
Autor: Muneki Yasuda, Kei Uchizawa
Publikováno v: Neural Computation. 33:1037-1062
Spatial Monte Carlo integration (SMCI) is an extension of standard Monte Carlo integration and can approximate expectations on Markov random fields with high accuracy. SMCI was applied to pairwise Boltzmann machine (PBM) learning, with superior resul
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a05ecaba80cba0f4f0320c86e4c9f40e
https://doi.org/10.1162/neco_a_01365
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