Regularization of extreme learning machines with information of spatial relations of the projected data
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Universidade Federal de Minas Gerais
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The following work presents a new approach to automatic selection of Tikhonov's regularization parameter, responsible for controlling the weight value of an ELM neural network. A strategy based on measurements obtained from data projection (Fisher-Score) is introduced. Seven datasets are tested and results are compared to those obtained when the regularization parameter is selected through cross-validation. The strategy shows satisfactory classification performance (in terms of p-value), while presenting significant training time reduction.
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Redes neurais (Computação)
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Neurons , Training , Cybernetics , Minimization , Testing , Biological neural networks , Aerospace electronics, Extreme Learning Machine , Training Time , Parameter Selection , Weight Decay , Regularization Parameter , Null Hypothesis , High-dimensional , Dimensional Space , Hidden Layer , Weight Matrix , Linear Discriminant Analysis , Weight Vector , Decision Boundary , Neurons In The Hidden Layer , Hidden Neurons , Affinity Matrix , Linearly Separable , Regularization Scheme , Class Mean , Universal Approximation , Norm Penalty , Fisher Score , Single Layer Feedforward Network , Empirical Risk Minimization
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https://ieeexplore.ieee.org/document/8820610