Curve and surface fitting with splines
Curve and surface fitting with splines
Regularization theory and neural networks architectures
Neural Computation
Automatic Detection of Premature Ventricular Contraction Using Quantum Neural Networks
BIBE '03 Proceedings of the 3rd IEEE Symposium on BioInformatics and BioEngineering
A Theory of Networks for Approximation and Learning
A Theory of Networks for Approximation and Learning
A hybrid learning scheme combining EM and MASMOD algorithms for fuzzy local linearization modeling
IEEE Transactions on Neural Networks
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This paper presents an effective learning scheme that combines B-spline modeling and regularized neural networks. Essential issues of structural design and learning process are discussed. Regularization theory is leveraged to design the topological structure of the network. A training algorithm is derived for the learning of both synaptic weights and B-spline coefficients. The approach is then applied to the medical problem of heart arrhythmia detection, particularly the detection of premature ventricular contraction. Promising results demonstrate the potential benefits of the proposed method.