Factor analysis was used to identify dietary patterns and linear regression models were used to (i) identify differences in food intake practices according to socioeconomic status and place of residence and (ii) establish relationships between dietary patterns and cardiovascular risk factors.
This article presents a new generalized feedforward neural network (GFNN) architecture for pattern classification and regression.
These techniques have been used to solve many pattern recognition and regression estimation problems and have been applied to the problems of dependency estimation, forecasting, and constructing intelligent machines.
In general, pattern classification and regression tasks do not take into consideration the variation in the importance of the training samples.
Recently, a novel machine learning technique, called support vector machine (SVM), has drawn much attention in the fields of pattern classification and regression forecasting.
Table 2 presents the component pattern matrix and regression coefficients for each variable on each of the components.
Several machine-learning algorithms were investigated in building a classifier to predict novel cancer genes, including naïve Bayes, logistic regression and support vector machines (SVM), all of which have been widely used for pattern classification and regression problems.
The normal pattern of modelling and regression depends on neural crest cells, although the exact mechanism remains unknown [3, 10, 13, 19].
Pre- and post-fire landscape patterns were assessed and regression tree analyses were used to identify factors influencing national-scale fire susceptibility.
The study employs random multinomial logit, a random forest of logit models, to rank the variables; expectation maximization clustering algorithm to identify crash prone traffic patterns and classification and regression trees to explain crash phenomena.
