Measuring effectiveness of a dynamic artificial neural network algorithm for classification problems

Ghiassi, M.; Burnley, C.

doi:10.1016/j.eswa.2009.09.017

Cited by 23 publications

(13 citation statements)

References 38 publications

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“…This approach generates hyperplanes to separate classes [53]. The boundaries of the hyperplane are represented by support vectors instead of a single boundary value.…”

Section: Methodsmentioning

confidence: 99%

Classification of Camellia (Theaceae) Species Using Leaf Architecture Variations and Pattern Recognition Techniques

Lü

Ghiassi

et al. 2012

PLoS ONE

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Leaf characters have been successfully utilized to classify Camellia (Theaceae) species; however, leaf characters combined with supervised pattern recognition techniques have not been previously explored. We present results of using leaf morphological and venation characters of 93 species from five sections of genus Camellia to assess the effectiveness of several supervised pattern recognition techniques for classifications and compare their accuracy. Clustering approach, Learning Vector Quantization neural network (LVQ-ANN), Dynamic Architecture for Artificial Neural Networks (DAN2), and C-support vector machines (SVM) are used to discriminate 93 species from five sections of genus Camellia (11 in sect. Furfuracea, 16 in sect. Paracamellia, 12 in sect. Tuberculata, 34 in sect. Camellia, and 20 in sect. Theopsis). DAN2 and SVM show excellent classification results for genus Camellia with DAN2's accuracy of 97.92% and 91.11% for training and testing data sets respectively. The RBF-SVM results of 97.92% and 97.78% for training and testing offer the best classification accuracy. A hierarchical dendrogram based on leaf architecture data has confirmed the morphological classification of the five sections as previously proposed. The overall results suggest that leaf architecture-based data analysis using supervised pattern recognition techniques, especially DAN2 and SVM discrimination methods, is excellent for identification of Camellia species.

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“…This approach generates hyperplanes to separate classes [53]. The boundaries of the hyperplane are represented by support vectors instead of a single boundary value.…”

Section: Methodsmentioning

confidence: 99%

Classification of Camellia (Theaceae) Species Using Leaf Architecture Variations and Pattern Recognition Techniques

Lü

Ghiassi

et al. 2012

PLoS ONE

Self Cite

View full text Add to dashboard Cite

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“…The constructed classifier for image features is based on the back-propagation learning algorithm which depends on the generalized least mean square (LMS) rule to minimize the average difference between the output and the target value in the neural network. Ghiassi and Burnley [224] have developed a dynamic artificial neural network (DAN2) as an alternative to traditional classification methods such as SVM and Bayesian. The DAN2 is based on 1) learning and knowledge accumulation at each layer, 2) adjusting and propagating this knowledge forward to the next layer, and 3) repeating these steps until the desired criteria for network performance are reached.…”

Section: ) Artificial Neural Network (Anns)mentioning

confidence: 99%

“…Unsupervised learning [212][213][214], Supervised learning [221][222][223][224][225][226][227][228], Deep learning [20,[229][230][231][232][233][234], Distance metric learning [263][264][265][266][267][268], Rank learning [302][303][304][305][306]. Automatic image tagging/annotation CNN-based [233], Group sparsity [294][295][296][297][298].…”

Section: The Use Of Machine Learningmentioning

confidence: 99%

Semantic content-based image retrieval: A comprehensive study

Alzu’bi

Amira

Ramzan

2015

Journal of Visual Communication and Image Representation

180

106

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“…In [5][6][7], the methods of modulation recognition utilizing the neural network classifier based on the back propagation (BP) algorithm were discussed. Improved algorithms of BP (e.g., correction of inertia weight, adjustment of learning rate, gradient optimization algorithms) are adopted on the purpose of overcoming the drawbacks that the convergence rate of BP algorithm is low and BP algorithm is easy to have a local minimum point.…”

Section: Introductionmentioning

confidence: 99%

Method of neural network modulation recognition based on clustering and Polak-Ribiere algorithm

Yang

et al. 2014

J. of Syst. Eng. Electron.

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To improve the recognition rate of signal modulation recognition methods based on the clustering algorithm under the low SNR, a modulation recognition method is proposed. The characteristic parameter of the signal is extracted by using a clustering algorithm, the neural network is trained by using the algorithm of variable gradient correction (Polak-Ribiere) so as to enhance the rate of convergence, improve the performance of recognition under the low SNR and realize modulation recognition of the signal based on the modulation system of the constellation diagram. Simulation results show that the recognition rate based on this algorithm is enhanced over 30% compared with the methods that adopt clustering algorithm or neural network based on the back propagation algorithm alone under the low SNR. The recognition rate can reach 90% when the SNR is 4 dB, and the method is easy to be achieved so that it has a broad application prospect in the modulating recognition.

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Measuring effectiveness of a dynamic artificial neural network algorithm for classification problems

Cited by 23 publications

References 38 publications

Classification of Camellia (Theaceae) Species Using Leaf Architecture Variations and Pattern Recognition Techniques

Classification of Camellia (Theaceae) Species Using Leaf Architecture Variations and Pattern Recognition Techniques

Semantic content-based image retrieval: A comprehensive study

Method of neural network modulation recognition based on clustering and Polak-Ribiere algorithm

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