Margin-Sparsity Trade-Off for the Set Covering Machine

We propose a new learning algorithm for the set covering machine and a tight data-compression risk bound that the learner can use for choosing the appropriate tradeoff between the sparsity of a classifier and the magnitude of its separating margin.

• Type: Conference
• Year: 2005
• Where: ECML
• Authors: François Laviolette, Mario Marchand, Mohak Shah
• Keywords: ECML 2005, Separating Margin, Set Covering Machine, Tight Data-compression Risk