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A comparison of these models was performed using the magnitude of the coefficient of determination R2 for the linear models and the value of the sum of normalised errors (SNE) for linear and non-linear models.
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The signature was then evaluated by computing the sum of normalised expression values for the upregulated genes minus the sum of the downregulated genes.
Unlike [15,16], we do not consider the quasi-error which is the weighted sum of error plus error estimator.
The magnitude of each component of the gradient of the normalised sum-of-squared errors (GNSSE) is smaller than a pre-specified value (nominally 10−3).
For errors, the sum of errors over the blocks was computed.
Sum of Squared Errors (SSE).
So as to minimize The sum of square errors (SQE).
Each hidden unit leads to the maximal reduction of the sum of squared errors.
Controller gains were optimized by minimizing a weighted sum of position errors, orientation errors, and muscle activations.
The sum of squared errors is used as classification criteria.
The overall prediction error is the sum of the errors for all n runs [ 21].
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