Verifying detected facial parts by multidirectional associative memory

Kitabata, Miki; Takefuji, Yoshiyasu

doi:10.1109/ipmm.1999.791517

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2014

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“…Hattori and Hagiwara applied MAMs to knowledge processing systems [2]. Kitabata et al [6] and Kitabata and Takefuji [7] applied MAMs to face recognition.…”

Section: Introductionmentioning

confidence: 99%

Multidirectional associative memory with self-connections

Furusawa

Kobayashi

2014

NOLTA

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Multidirectional associative memory (MAM) enables associations among many items. Its architecture is very simple and provides high parallelism. However, recall results of MAMs are inconsistent for contradictory inputs. The recall results depend on the order of update. If a given input includes an incorrect pattern, we expect that the incorrect pattern will be corrected, and the recalled pattern is determined by the majority. In this work, we propose MAMs with self-connections and a new learning algorithm. These MAMs provide recall results independent of the order of update. Furthermore, they maintain the advantages of MAMs, such as their simple architecture, high parallelism and stability.

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“…Hattori and Hagiwara applied MAMs to knowledge processing systems [2]. Kitabata et al [6] and Kitabata and Takefuji [7] applied MAMs to face recognition.…”

Section: Introductionmentioning

confidence: 99%

Multidirectional associative memory with self-connections

Furusawa

Kobayashi

2014

NOLTA

View full text Add to dashboard Cite

show abstract

Verifying detected facial parts by multidirectional associative memory

Cited by 1 publication

References 5 publications

Multidirectional associative memory with self-connections

Multidirectional associative memory with self-connections

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