Stable Transductive Learning

Abstract. We develop a new error bound for transductive learning algorithms. The slack term in the new bound is a function of a relaxed notion of transductive stability, which measures the sensitivity of the algorithm to most pairwise exchanges of training and test set points. Our bound is based on a novel concentration inequality for symmetric functions of permutations. We also present a simple sampling technique that can estimate, with high probability, the weak stability of transductive learning algorithms with respect to a given dataset. We demonstrate the usefulness of our estimation technique on a well known transductive learning algorithm.

• Type: Conference
• Year: 2006
• Where: COLT
• Authors: Ran El-Yaniv, Dmitry Pechyony
• Keywords: COLT 2006, Machine Learning, Test Set Points, Transductive Learning Algorithm, Transductive Stability