By placing the CEA in a regression framework, we will be able to use a wide variety of regression tools while exploring key patient characteristics and using regression model diagnostic tools [ 120].
The same argument follows that of the use of multivariate regression tools for predicting reservoir properties that the features are extremely nonlinear attributes and could not be adequately modeled with such linear relationships.
Overall, the representation of relationships using classification and regression tools can be considered a useful approach to assess the state of aquatic ecosystems and effectively determine significant stressor variables.
The whole process can be automated using standard linear regression tools.
This study proposes a novel methodology to remove aforementioned problems by use of support vector regression tool originally invented by Vapnik (1995, The Nature of Statistical Learning Theory. Springer, New York, NY).
A multi-layer artificial neural network (ANN) is used as regression tool to model the combined effect of solute structure and column on retention.
From the selected risk factors, with the use of statistic tools, logistic regression and ordinal logistic regression, the variables were selected characterized with statistically essential influence on occurrence of NE episodes in children.
The mapping functions supporting the prediction system are modeled using SVM as regression tool.
We used also the non-linear regression tools of Sigma-Plot software (Version 11).
This implies that big data entails the use of tools (classification, clustering, regression algorithms, etc)., techniques (data mining, machine learning, statistical analysis, etc)., and technologies (Hadoop, HBase, MongoDB, etc).
