By Giannopoulou E. (ed.)
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As a rule I believe the 2 prior stories. This booklet isn't sturdy as an advent. First learn another publication reminiscent of Setubal and Meidanis, "Introduction to Computational Molecular Biology"; or Krane & Raymer, "Fundamental techniques of Bioinformatics". those books have extra readable narrative and examples.
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Extra resources for Data mining in medical and biological research
These errors are used to update the network weights. The amount of weights to be added to or subtracted from the previous weight is governed by the delta rule. After the knowledge representation is determined, the BP neural network will be trained to attempt the classification behavior. The number of hidden layers and the number of nodes in each hidden layer are determined during the training phase. In this study, a fully connected feedforward neural network was used, and its network parameters and stopping criterion were set.
When compared to STFT, the advantage of time-frequency localisation is that wavelet analysis varies the time-frequency aspect ratio, producing good frequency localization at low frequencies (long time windows), and good time localisation at high frequencies (short time windows). This produces a segmentation, or tiling of the time-frequency plane that is appropriate for most physical signals, especially those of a transient nature. The wavelet technique applied to the EEG signal will reveal features related to the transient nature of the signal, which are not obvious by the Fourier, transform.
The decomposition of the signal into the different frequency bands is merely obtained by consecutive high-pass and low-pass filtering of the time domain signal. The procedure of multi-resolution decomposition of a signal x[n] is schematically shown in Fig 4. Each stage of this scheme consists of two digital filters and two down-samplers by 2. ] is its mirror version, low-pass in nature. The down-sampled outputs of first high-pass and low-pass filters provide the detail, D1 and the approximation, A1, respectively.
Data mining in medical and biological research by Giannopoulou E. (ed.)