loss function


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loss function

[′lȯs ‚fəŋk·shən]
(mathematics)
In decision theory, the function, dependent upon the decision and the true underlying distributions, which expresses the loss produced in taking the decision.
McGraw-Hill Dictionary of Scientific & Technical Terms, 6E, Copyright © 2003 by The McGraw-Hill Companies, Inc.
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The nominal rate is set by a central bank (ECB) that minimises a quadratic loss function (LF) for the monetary union, without taking into consideration financial stability:
Compared with [16], we adopt the [L.sub.2,q] norm instead of Frobenius norm in loss function to reduce the influence of noise and edge information.
Each branch learns task-specific features and has its own loss function corresponding to each task.
We use [L.sub.2,1] matrix norm based loss function as the reconstruction loss.
These methods require the use of a loss function, which measures the difference between the parameter and its estimator.
A general classifier is usually trained from supervised learning, which focuses on teaching the label information to the classifier and minimizes the designed loss function; therefore, it has learned specific feature mapping for each object.
Figures 13 and 14 compare and analyze the change of loss function value under different learning rates in the whole training process and make a brief analysis and explanation.
The propagation loss function represents the variation in that range, and the correlation distance will be short because the loss function has more fluctuation as distance change.
To train the DNN, we used the mean square error (MSE) loss function to measure the error between the predicted, and the truth vertex positions:
Assuming that the target value of Q is denoted by y, thus the loss function of Q-network is yielded: