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Although Deep Convolutional Neural Networks (CNNs) have liberated their power in various computer vision tasks, the most important components of CNN, convolutional layers and fully connected layers, are still limited to linear transformations. In this paper, we propose a novel Factorized Bilinear (FB) layer to model the pairwise feature interactions by considering the quadratic terms in the transformations...
An information adaptive test system of student's knowledge was proposed. It is based on the three-criterion decision-making model of transferring between the test difficulties levels using neural network. Knowledge check results in the groups of students studying in the distance learning system Moodle were compared with knowledge check results groups of students who were trained using the improved...
One of the disadvantages of using Artificial Neural Networks (ANNs) is their significant training time need, which scales with the complexity of the network and with the complexity of the problem that is needed to be solved. Radial Basis Function Neural Networks (RBFNNs) are neural networks that use the linear combination of radial basis functions, utilizing hybrid learning procedures which can solve...
The number of hidden neurons has a great influence on the generalization capability of Multilayer Perceptron Neural Network (MLPNN). The ultimate goal of building a MLPNN is to recognize (or generalize) future unseen sample correctly based on the training from training samples. Therefore, the Localized Generalization Error Model (L-GEM) is adopted in this work to select the architecture of a MLPNN...
In this paper, an adaptive evolutionary multi-objective selection method of RBF Networks structure is discussed. The candidates of RBF Network structures are encoded into particles in Particle Swarm Optimization (PSO). These particles evolve toward Pareto-optimal front defined by several objective functions with model accuracy and complexity. The problem of unsupervised and supervised learning is...
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