Sentence examples for risk minimization principles from inspiring English sources

The different time-series prediction techniques use different risk minimization principles to create the prediction model.

Four sub-hypotheses are introduced in order to experiment the risk minimization principles vis-à-vis the different workload patterns.

Structural risk minimization principles are used in its implementation that results in superior performance in regression problems as well as classification technique.

To test these sub-hypotheses, the theoretical fundamentals of the prediction algorithms were investigated through analyzing the learning theory and the risk minimization principles.

The main goal of the experiment presented in this section is to verify the empirical and the structural risk minimization principles behaviors in the environments with the periodic, growing, and unpredictable workload patterns.

To test the proposition, a comprehensive theoretical investigation is provided on different risk minimization principles and their effects on the accuracy of the time-series prediction techniques in the cloud environment.

The second unique characteristic of SVM is that it is established on the unique theory of the structural risk minimization principle to estimate a function by minimizing an upper bound of the generalization error and therefore very resistant to the overfitting problem, eventually achieving a high generalization performance.

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.

Support vector machine (SVM) is a classification method based on the structured risk minimization principle.

Policy Optimizer for Exponential Models is a simple gradient optimizer for learning structured output models (like Conditional Random Fields) using the Counterfactual Risk Minimization principle.

In the recognition of shale lithofacies, the application of support vector machine (SVM), which underlies statistical learning theory and structural risk minimization principle, is superior to the traditional empirical risk minimization principle employed by artificial neural network (ANN).