The main challenge of cluster analysis is that the number of clusters or the number of model parameters is seldom known, and it must therefore be determined before clustering. Bay...
Suppose that the only available information in a multi-class problem are expert estimates of the conditional probabilities of occurrence for a set of binary features. The aim is t...
Ludmila I. Kuncheva, Christopher J. Whitaker, Pete...
L1 (also referred to as the 1-norm or Lasso) penalty based formulations have been shown to be effective in problem domains when noisy features are present. However, the L1 penalty...
— An iterative feature selection method based on feature typicality and interactivity analysis is presented in this paper. The aim is to enhance model interpretability by selecti...
Cyril Mazaud, Jan Rendek, Vincent Bombardier, Laur...
In supervised kernel methods, it has been observed that the performance of the SVM classifier is poor in cases where the diagonal entries of the Gram matrix are large relative to ...