Systems that learn: an introduction to learning theory for cognitive and computer scientists
Systems that learn: an introduction to learning theory for cognitive and computer scientists
Identification of unions of languages drawn from an identifiable class
COLT '89 Proceedings of the second annual workshop on Computational learning theory
Conflict Resolution as Discovery in Particle Physics
Machine Learning
Inductive inference of monotonic formal systems from positive data
New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
The correct definition of finite elasticity: corrigendum to identification of unions
COLT '91 Proceedings of the fourth annual workshop on Computational learning theory
On the role of procrastination in machine learning
Information and Computation
On the intrinsic complexity of learning
Information and Computation
The intrinsic complexity of language identification
Journal of Computer and System Sciences
Ordinal mind change complexity of language identification
Theoretical Computer Science
The synthesis of language learners
Information and Computation
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Mind change complexity of learning logic programs
Theoretical Computer Science
Language Learning with a Bounded Number of Mind Changes
STACS '93 Proceedings of the 10th Annual Symposium on Theoretical Aspects of Computer Science
Inductive Inference with Bounded Mind Changes
ALT '92 Proceedings of the Third Workshop on Algorithmic Learning Theory
Derived Sets and Inductive Inference
AII '94 Proceedings of the 4th International Workshop on Analogical and Inductive Inference: Algorithmic Learning Theory
Learning, Logic, and Topology in a Common Framework
ALT '02 Proceedings of the 13th International Conference on Algorithmic Learning Theory
Justification as truth-finding efficiency: how Ockham's Razor works
Minds and Machines - Machine learning as experimental philosophy of science
Mind change efficient learning
COLT'05 Proceedings of the 18th annual conference on Learning Theory
On a syntactic characterization of classification with a mind change bound
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Compute inclusion depth of a pattern
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Topological Properties of Concept Spaces
ALT '08 Proceedings of the 19th international conference on Algorithmic Learning Theory
Topological properties of concept spaces (full version)
Information and Computation
Mind change optimal learning of Bayes net structure
COLT'07 Proceedings of the 20th annual conference on Learning theory
Set systems: Order types, continuous nondeterministic deformations, and quasi-orders
Theoretical Computer Science
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This paper studies efficient learning with respect to mind changes. Our starting point is the idea that a learner that is efficient with respect to mind changes minimizes mind changes not only globally in the entire learning problem, but also locally in subproblems after receiving some evidence. Formalizing this idea leads to the notion of strong mind change optimality. We characterize the structure of language classes that can be identified with at most @a mind changes by some learner (not necessarily effective): a language class L is identifiable with @a mind changes iff the accumulation order of L is at most @a. Accumulation order is a classic concept from point-set topology. We show that accumulation order is related to other established notions of structural complexity, such as thickness and intrinsic complexity. To aid the construction of learning algorithms, we show that the characteristic property of strongly mind change optimal learners is that they output conjectures (languages) with maximal accumulation order. We illustrate the theory by describing strongly mind change optimal learners for various problems such as identifying linear subspaces, one-variable patterns, and fixed-length patterns.