PAC-Bayes Learning of Conjunctions and Classification of Gene-Expression Data

Part of Advances in Neural Information Processing Systems 17 (NIPS 2004)

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Mario Marchand, Mohak Shah


We propose a “soft greedy” learning algorithm for building small conjunctions of simple threshold functions, called rays, defined on single real-valued attributes. We also propose a PAC-Bayes risk bound which is minimized for classifiers achieving a non-trivial tradeoff between sparsity (the number of rays used) and the mag- nitude of the separating margin of each ray. Finally, we test the soft greedy algorithm on four DNA micro-array data sets.