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Table 1 Symbols and Descriptions

From: Testing coverage criteria for optimized deep belief network with search and rescue

Symbols Descriptions
\(g_{h}\) The amount of hidden units
\(g_{v}\) The amount of visible units
\(W\) Simultaneous weights among hidden and visible units
\(L\) Simultaneous weights among visible and visible units
\(J\) Simultaneous weights among hidden and hidden units
\(v\) Visible unit
\(h\) Hidden unit
\(W_{ij}\) Symmetric connection among hidden (\(j\)) and visible (\(i\)) unit
\(b_{j}\) and \(a_{i}\) Bias terms
\(Z\) Normalization constant
\(m\) Amount of training data samples
\(X_{il}\) \(i\;th\) unit of the \(l\;th\) data occasion
\(\varepsilon\) Learning rate
\(g(x) = 1/(1 + \exp ( - x))\) Logistic sigmoid function
\(D\) Entire amount of data samples
\(O_{z}^{e}\) Expected output
\(Z_{z}^{e}\) Predicted output
\(M_{n}\) Location of the \(n\;th\) stored clue
\(N = \left\{ {n_{1} ,\,n_{2} ,\,...} \right\}\) Set of neurons
\(T = \left\{ {x_{1} ,\,x_{2} ,\,...} \right\}\) Test inputs
\(L_{i}\) Arrangement of neurons on the \(i^{th}\) layer
\(X_{i}\) Preceding location
\(T\) Chosen features
\(X\) Weight
\(Low_{n}\) Lower boundary output values for a neuron \(n\)
\(High_{n}\) Upper boundary output values for a neuron \(n\)