Proceedings Article10.1109/SFCS.1989.63454
Learning binary relations and total orders
Sally A. Goldman,Ronald L. Rivest,Robert E. Schapire +2 more
- 30 Oct 1989
- pp 46-51
TL;DR: The problem of designing polynomial prediction algorithms for learning binary relations is studied for an online model in which the instances are drawn by the learner, by a helpful teacher, by an adversary, or according to a probability distribution on the instance space.
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Abstract: The problem of designing polynomial prediction algorithms for learning binary relations is studied for an online model in which the instances are drawn by the learner, by a helpful teacher, by an adversary, or according to a probability distribution on the instance space. The relation is represented as an n*m binary matrix, and results are presented when the matrix is restricted to have at most k distinct row types, and when it is constrained by requiring that the predicate form a total order. >
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Information, Divergence and Risk for Binary Experiments
TL;DR: The new viewpoint also illuminates existing algorithms: it provides a new derivation of Support Vector Machines in terms of divergences and relates maximum mean discrepancy to Fisher linear discriminants.
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Learning regular sets from queries and counterexamples
TL;DR: In this article, the problem of identifying an unknown regular set from examples of its members and nonmembers is addressed, where the regular set is presented by a minimaMy adequate teacher, which can answer membership queries about the set and can also test a conjecture and indicate whether it is equal to the unknown set and provide a counterexample if not.
Queries and Concept Learning
TL;DR: This work considers the problem of using queries to learn an unknown concept, and several types of queries are described and studied: membership, equivalence, subset, superset, disjointness, and exhaustiveness queries.
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