Hidden Markov models have become the preferred technique for visual recognition of human gestures. However, the recognition rate depends on the set of visual features used, and al...
The attribute selection techniques for supervised learning, used in the preprocessing phase to emphasize the most relevant attributes, allow making models of classification simple...
LASSO (Least Absolute Shrinkage and Selection Operator) is a useful tool to achieve the shrinkage and variable selection simultaneously. Since LASSO uses the L1 penalty, the optim...
The nearest shrunken centroid classifier uses shrunken centroids as prototypes for each class and test samples are classified to belong to the class whose shrunken centroid is nea...
Variable selection problems are typically addressed under a penalized optimization framework. Nonconvex penalties such as the minimax concave plus (MCP) and smoothly clipped absol...