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Found 5 papers, 2 papers with code
Minimax Classification with 0-1 Loss and Performance Guarantees
We also present MRCs' finite-sample generalization bounds in terms of training size and smallest minimax risk, and show their competitive classification performance w. r. t.
Kernels of Mallows Models under the Hamming Distance for solving the Quadratic Assignment Problem
The Quadratic Assignment Problem (QAP) is a well-known permutation-based combinatorial optimization problem with real applications in industrial and logistics environments.
Rank aggregation for non-stationary data streams
Then, we generalize the whole family of weighted voting rules (the family to which Borda belongs) to situations in which some rankings are more \textit{reliable} than others and show that this generalization can solve the problem of rank aggregation over non-stationary data streams.
Supervised classification via minimax probabilistic transformations
Conventional techniques for supervised classification constrain the classification rules considered and use surrogate losses for classification 0-1 loss.
General Supervision via Probabilistic Transformations
Different types of training data have led to numerous schemes for supervised classification.
