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Depending on the system design, regression models may be used instead of classification.
The benefit of using learning-to-rank instead of classification methods is that we predict exactly T links by considering the top-T pairs of our ranking.
That is why a more logical approach should be to broaden its scope instead of classification or marginalization to capture more of its diversity.
The novelty of the approach is to focus in prototype selection for regression problems instead of classification as the majority of the literature deals only with the last one.
In this paper we present a novel approach for computer-aided detection which faces the class imbalance with a cascade of boosting classifiers where each node is trained by a learning algorithm based on ranking instead of classification error.
Regression trees (instead of classification trees) were used because the dependent variable (temperature or precipitation) was continuous.
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The softmax regression is a generalized version of the logistic regression [13, 14, 15, 16], where the output class labels are multi-class classification instead of binary classification.
The softmax regression is generalized version of the logistic regression [9, 10, 11, 12], where the output class labels are multi-class classification instead of binary classification as done in the logistic regression.
They formulated the problem as a single structured-output hierarchical classification (HC) instead of binary classification, but its predictive performance for sparsely annotated functional labels is far from satisfactory (Clark and Radivojac, 2013; Eisner et al., 2005; Guan et al., 2008; Jiang et al., 2014; Kim et al., 2008; Obozinski et al., 2008; Sokolov and Ben-Hur, 2010).
Instead of traditional classification routines a fuzzy method was developed.
These are shown here instead of under classification table (Table 4).
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