Mistake bounds and logarithmic linear-threshold learning algorithms
Mistake bounds and logarithmic linear-threshold learning algorithms
COLT '90 Proceedings of the third annual workshop on Computational learning theory
Machine Learning
Machine Learning
Learning binary relations and total orders
SFCS '89 Proceedings of the 30th Annual Symposium on Foundations of Computer Science
On the complexity of learning from counterexamples
SFCS '89 Proceedings of the 30th Annual Symposium on Foundations of Computer Science
On the complexity of learning from counterexamples and membership queries
SFCS '90 Proceedings of the 31st Annual Symposium on Foundations of Computer Science
Information and Computation
Information and Computation
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We consider a generalization of the mistake-bound model (for learning {0, 1}-valued functions) in which the learner must satisfy a general constraint on the number M"+ of incorrect 1 predictions and the number M"- of incorrect 0 predictions. We describe a general-purpose optimal algorithm for our formulation of this problem. We describe several applications of our general results, involving situations in which the learner wishes to satisfy linear inequalities in M"+ and M"-.