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Found 7 papers, 1 papers with code
Remarks on Loss Function of Threshold Method for Ordinal Regression Problem
Threshold methods are popular for ordinal regression problems, which are classification problems for data with a natural ordinal relation.
Parallel Algorithm for Optimal Threshold Labeling of Ordinal Regression Methods
For $K$-class OR tasks, threshold methods learn a one-dimensional transformation (1DT) of the explanatory variable so that 1DT values for observations of the explanatory variable preserve the order of label values $1,\ldots, K$ for corresponding observations of the target variable well, and then assign a label prediction to the learned 1DT through threshold labeling, namely, according to the rank of an interval to which the 1DT belongs among intervals on the real line separated by $(K-1)$ threshold parameters.
Convergence Analysis of Blurring Mean Shift
Blurring mean shift (BMS) algorithm, a variant of the mean shift algorithm, is a kernel-based iterative method for data clustering, where data points are clustered according to their convergent points via iterative blurring.
Label Smoothing is Robustification against Model Misspecification
For example, in binary classification, instead of the one-hot target $(1, 0)^\top$ used in conventional logistic regression (LR), LR with LS (LSLR) uses the smoothed target $(1-\frac{\alpha}{2},\frac{\alpha}{2})^\top$ with a smoothing level $\alpha\in(0, 1)$, which causes squeezing of values of the logit.
Convergence Analysis of Mean Shift
The mean shift (MS) algorithm seeks a mode of the kernel density estimate (KDE).
Optimal Kernel for Kernel-Based Modal Statistical Methods
Kernel-based modal statistical methods include mode estimation, regression, and clustering.
Kernel Selection for Modal Linear Regression: Optimal Kernel and IRLS Algorithm
Modal linear regression (MLR) is a method for obtaining a conditional mode predictor as a linear model.
