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Found 4 papers, 1 papers with code
Quick sensitivity analysis for incremental data modification and its application to leave-one-out CV in linear classification problems
This property is quite advantageous in a typical sensitivity analysis task where only a small number of instances are updated.
Regularization Path of Cross-Validation Error Lower Bounds
Careful tuning of a regularization parameter is indispensable in many machine learning tasks because it has a significant impact on generalization performances.
An Algorithmic Framework for Computing Validation Performance Bounds by Using Suboptimal Models
If a reasonably good suboptimal model is available, our algorithm can compute lower and upper bounds of many useful quantities for making inferences on the unknown target model.
Safe Sample Screening for Support Vector Machines
Sparse classifiers such as the support vector machines (SVM) are efficient in test-phases because the classifier is characterized only by a subset of the samples called support vectors (SVs), and the rest of the samples (non SVs) have no influence on the classification result.
