کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10127090 | 1645032 | 2018 | 25 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A novel training method to preserve generalization of RBPNN classifiers applied to ECG signals diagnosis
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
In this paper a novel training technique is proposed to offer an efficient solution for neural network training in non-trivial and critical applications such as the diagnosis of health threatening illness. The presented technique aims to enhance the generalization capability of a neural network while preserving its sensitivity and precision. The implemented method has been devised in order to slowly increase, during training, the generalization capabilities of a Radial Basis Probabilistic Neural Network classifier, as well as preventing it from over-generalization and the consequent lack of resulting classification performances. The developed method was tested on Electrocardiograms. These latter are generally considered non-trivial both due to the difficulty to recognize some anomalous heart activities, and due to the intermittent nature of abnormal beat occurrences. The implemented training method obtained satisfactory performances, sensitivity and precision while showing high generalization capabilities.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neural Networks - Volume 108, December 2018, Pages 331-338
Journal: Neural Networks - Volume 108, December 2018, Pages 331-338
نویسندگان
Francesco Beritelli, Giacomo Capizzi, Grazia Lo Sciuto, Christian Napoli, Marcin Woźniak,