Appearance-based approach category is considered as any image-based approach face detector that does not employ the boosting classification methods in it classification stage.
The high computational speed of boosting classification makes it attractive for real-time fMRI to facilitate online interpretation of dynamically changing activation patterns.
The aim of this study was to predict soil properties at a regional scale by parametrizing soil-landscape models using a machine-learning method recently applied to soil science concerns: boosted classification and regression trees.
In Section 5, we extend our previous results by comparing boosting and bagging in terms of both classification and recognition performance and show, interestingly, that bagging achieves the same reduction in recognition error rates as boosting, even though it cannot match boosting classification error rate reduction.
Random forests (RF), ensembles of feedforward neural networks (NN), support vector machines (SVM), ensembles of boosted classification stumps (MB), k-nearest neighbor classification (k-NN) and linear discriminant analysis (LDA) are evaluated with various AD measures on ten different benchmark data sets.
For the main analyses, boosted classification trees were constructed by MART® to identify non-redundant prognostic variables, which were then further analyzed by CART to identify thresholds that would define them as categorical variables.
Introduction of cutoff values for each diagnostic group was determined by group membership probabilities obtained from gradient boosting classification (Fig. 3B, C).
Recognizing that some characteristics can be interdependent, we performed multivariate analyses with two approaches whereby interactions among variables are emphasized: recursive partitioning via receiver operating characteristic (RP-ROC) [ 26] curves and (boosted) classification and regression trees (CART®) [ 24, 27, 28].
Significance of differentially methylated loci is given as relative variable importance (Fig. 3B, C) reflecting the number of decisions from gradient boosting classification made on basis of each candidate loci.
We constructed three models to predict NSLN status: recursive partitioning with receiver operating characteristic curves (RP-ROC), boosted Classification and Regression Trees (CART), and multivariate logistic regression (MLR) informed by CART.
