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In comparison to traditional monolithic neural networks, both the proposed architectures improve classification accuracy and minimize the training complexity.
This membership function will get tuned or adjusted during backward learning process of ANFIS to minimize the training error.
First, we show that the proposed framework can be used to minimize the training energy budget subject to a quality constraint on the MSE of the channel estimator.
By solving the above quadratic programming problem, the SVM tries to maximize the margin between the data points in the two classes and to minimize the training errors simultaneously.
The factors hindering person's participation in the training program require further study in order to both optimize entry and to minimize the training attrition rate.
The ST was conducted on the first day, which was followed by a 3-day rest aimed to minimize the training effects.
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Moreover, a nonlinear optimal control problem is formulated to determine the optimal switched control design as automatic train regulation to minimize the train delays and control cost.
Firstly, we formulate an integer programming model with real-world speed profiles to minimize the trains' energy consumption with dwell time control.
The goal was minimizing the training computational cost and improving prediction.
This is likely due to the existence of structural risk minimization principle of SVM which is embodied in SVM algorithm and effectively minimizes upper bound of the generalization error, rather than minimizing the training error.
Specifically, we model feature learning as an evolution process governed by a PDE, which is designed to be translationally and rotationally invariant and is learned via minimizing the training error, hence extracts discriminative information from data.
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