A two-level Hamming network for high performance associative memory

Ikeda, Nobuhiko; Watta, Paul; Artiklar, M.; Hassoun, M.H.

doi:10.1016/s0893-6080(01)00089-2

Cited by 55 publications

(52 citation statements)

References 15 publications

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“…An associative memory is a memory that is addressed using its contents. When an input pattern, called a memory key, is presented, the associative memory should return a stored memory pattern coincident with the key (Ikeda et al 2001). The coincidence between a memory key and a stored pattern need not and should not be perfect.…”

Section: Electoral College Framework and Stabilitymentioning

confidence: 99%

A unified framework for improving the accuracy of all holistic face identification algorithms

Chen

Tokuda²

2009

Artif Intell Rev

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Reconstructing the challenging human face identification process as a stability problem, we show that Electoral College can be used as a framework that provides a significantly enhanced face identification process by improving the accuracy of all holistic algorithms. The results are demonstrated by extensive experiments on benchmark face databases applying the Electoral College framework embedded with standard baseline and newly developed face identification algorithms.

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Section: Electoral College Framework and Stabilitymentioning

confidence: 99%

A unified framework for improving the accuracy of all holistic face identification algorithms

Chen

Tokuda²

2009

Artif Intell Rev

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“…However, the Hamming neural network allows for a much more practical hardware implementation and faster retrievals by utilizing Hamming distance measures (Chou 1989;Ho et al 1994;Ikeda et al 2001). Both systems are applied to computer-based pattern recognition, especially when quality differences are more important than quantity differences (Brause 1995;Huang and Wechsler 1999;Manjunath et al 1996;Rowley et al 1996).…”

Section: The Hnnmentioning

confidence: 99%

Ordinal preference and inter-rater pattern recognition: Hopfield neural network vs. measures of association

Yen

2008

Artif Intell Rev

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In decision analysis, if the criterion is an ordinal rather than a cardinal one, a preferential solution depends on the inter-rater agreement. The Kendall coefficient of concordance W , the Friedman ranks statistic F r , and the Page L statistic are often used to determine the association among M sets of rankings. However, they may get some anomalies because they all use the cardinal variable "variance" to judge the association. In order to correct the anomalies, we use the modified Hopfield neural network instead to determine the association among M sets of rankings. The results are not only to reduce the unidentified cases but also to solve the anomalies.

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“…Refer for example to [15,16]. In [15], for example, the authors describe a two-level decoupled Hamming network for high-performance associative memory.…”

Section: Remarksmentioning

confidence: 99%

A New Two-Level Associative Memory for Efficient Pattern Restoration

Cruz¹,

Sossa²,

Barron³

2006

Neural Process Lett

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Pattern reconstruction or pattern restoration in the presence of noise is a main problem in pattern recognition. An essential feature of the noise acting on a pattern is its local nature. If a pattern is split into enough sub-patterns, a few of them will be less or more affected by noise, others will remain intact. In this paper, we propose a simple but effective methodology that exploits this fact for the efficient restoration of a pattern. A pattern is restored if enough of its sub-patterns are also restored. Since several patterns can share the same sub-patterns, the final decision is accomplished by means of a voting mechanism. Before deciding if a sub-pattern belongs to a pattern, sub-pattern restoration in the presence of noise is done by an associative memory. Numerical and real examples are given to show the effectiveness of the proposal. Formal conditions under which the proposal guaranties perfect restoration of a pattern from an unaltered or and altered version of it are also given.

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A two-level Hamming network for high performance associative memory

Cited by 55 publications

References 15 publications

A unified framework for improving the accuracy of all holistic face identification algorithms

A unified framework for improving the accuracy of all holistic face identification algorithms

Ordinal preference and inter-rater pattern recognition: Hopfield neural network vs. measures of association

A New Two-Level Associative Memory for Efficient Pattern Restoration

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