An RCE-based Associative Memory with Application to Human Face Recognition

Mu, Xiaoyan; Artiklar, M.; Watta, Paul; Hassoun, M.H.

doi:10.1007/s11063-006-9001-1

Cited by 16 publications

(8 citation statements)

References 16 publications

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“…In addition, neural associative memories are potentially useful in technical applications such as cluster analysis, speech and object recognition, or information retrieval in large databases [14], [15], [16], [17], [18], [19], [20], [21].…”

Section: Introductionmentioning

confidence: 99%

Optimal synaptic learning in non-linear associative memory

Knoblauch

2010

The 2010 International Joint Conference on Neural Networks (IJCNN)

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Neural associative memories are single layer perceptrons with fast synaptic learning typically storing discrete associations between pairs of neural activity patterns. For linear learning such as employed in Hopfield-type networks it is well known that the so-called covariance rule is optimal resulting in minimal output noise and maximal storage capacity. On the other hand, numerical simulations suggest that nonlinear rules such as clipped Hebbian learning in Willshaw-type networks perform better, at least for sparse neural activity and finite network size. Here I show that the Willshaw and Hopfield models are only limit cases of a general optimal model where synaptic learning is determined by probabilistic Bayesian considerations. Asymptotically, for large networks and very sparse neuron activity the Bayesian model becomes identical to an inhibitory implementation of the Willshaw model. Similarly, for less sparse patterns, the Bayesian model becomes identical to the Hopfield network employing the covariance rule. For intermediate sparseness or finite networks the optimal Bayesian rule differs from both the Willshaw and Hopfield models and can significantly improve memory performance.

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Section: Introductionmentioning

confidence: 99%

Optimal synaptic learning in non-linear associative memory

Knoblauch

2010

The 2010 International Joint Conference on Neural Networks (IJCNN)

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“…Zhang et al [79] proposed a modular face recognition scheme by combining the wavelet subband representations and kernel associative memories. An associative memory based on the restricted Coulomb energy was also applied to human face recognition [49]. Namba and Zhang [50] devised an associative memory to recognize Braille images.…”

Section: Introductionmentioning

confidence: 99%

Financial distress prediction using the hybrid associative memory with translation

Cleofas-Sánchez

García

Marqués

et al. 2016

Applied Soft Computing

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This paper presents an alternative technique for financial distress prediction systems. The method is based on a type of neural network, which is called hybrid associative memory with translation. While many different neural network architectures have successfully been used to predict credit risk and corporate failure, the power of associative memories for financial decision-making has not been explored in any depth as yet. The performance of the hybrid associative memory with translation is compared to four traditional neural networks, a support vector machine and a logistic regression model in terms of their prediction capabilities. The experimental results over nine real-life data sets show that the associative memory here proposed constitutes an appropriate solution for bankruptcy and credit risk prediction, performing significantly better than the rest of models under class imbalance and data overlapping conditions in terms of the true positive rate and the geometric mean of true positive and true negative rates.

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“…Although typical applications of these connectionist models include image recognition and recovery, data analysis, control, inference and prediction (Danilo et al, 2015;Kareem and Jantan, 2011;Lou and Cui, 2007;Mu et al, 2006;Nazari et al, 2014;Štanclová and Zavoral, 2005), the associative memories have lately emerged as useful classifiers for a large variety of problems in data mining and computational intelligence (AldapePérez et al, 2012(AldapePérez et al, , 2015Sharma et al, 2008;Uriarte-Arcia et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Associative learning on imbalanced environments: An empirical study

Cleofas-Sánchez

Sánchez

García

et al. 2016

Expert Systems with Applications

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Associative memories have emerged as a powerful computational neural network model for several pattern classification problems. Like most traditional classifiers, these models assume that the classes share similar prior probabilities. However, in many real-life applications the ratios of prior probabilities between classes are extremely skewed. Although the literature has provided numerous studies that examine the performance degradation of renowned classifiers on different imbalanced scenarios, so far this effect has not been supported by a thorough empirical study in the context of associative memories. In this paper, we fix our attention on the applicability of the associative neural networks to the classification of imbalanced data. The key questions here addressed are whether these models perform better, the same or worse than other popular classifiers, how the level of imbalance affects their performance, and whether distinct resampling strategies produce a different impact on the associative memories. In order to answer these questions and gain further insight into the feasibility and efficiency of the associative memories, a large-scale experimental evaluation with 31 databases, seven classification models and four resampling algorithms is carried out here, along with a non-parametric statistical test to discover any significant differences between each pair of classifiers.

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An RCE-based Associative Memory with Application to Human Face Recognition

Cited by 16 publications

References 16 publications

Optimal synaptic learning in non-linear associative memory

Optimal synaptic learning in non-linear associative memory

Financial distress prediction using the hybrid associative memory with translation

Associative learning on imbalanced environments: An empirical study

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