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Table1 Description of the most important notations

From: Flight delay prediction based on deep learning and Levenberg-Marquart algorithm

Notation

Description

Notation

Description

X

Input matrix

\({\hat{\text{X}}}\)

Reconstruct-ed \({\tilde{\text{X}}}\)

Min(x)

Lowest value in series and its number is zero

WT

Transposition of the weight matrix W

Max(x)

Highest number in series and has value of 1

bh

Show the bias associated with each hidden code

\({\text{X}}_{{\text{i}}}^{1}\)

Normalized data in first layer

L (X, \({\tilde{\text{X}}}\))

Reconstruct-ion error rate

h

Hidden layer

cost

Error rate

\({\tilde{\text{X}}}\)

Corrupted input

W

Weighted matrix

c

Corruption level

y

Output per x

H

Activation function

\({\hat{\text{y}}}\)

Output per \({\hat{\text{X}}}\)

\({\text{O}}_{{{\text{j}} - 1}} \to {\text{O}}_{{\text{j}}}\)

Each layer’s input is from previous layer’s output

θ

Is the parameters of the denoising autoencoder

b

Bias vector

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