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
Theory of recursive functions and effective computability
Theory of recursive functions and effective computability
Monotonic and non-monotonic inductive inference
New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
Language learning in dependence on the space of hypotheses
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
Characterizations of monotonic and dual monotonic language learning
Information and Computation
Monotonic and dual monotonic language learning
Theoretical Computer Science
A Guided Tour Across the Boundaries of Learning Recursive Languages
Algorithmic Learning for Knowledge-Based Systems, GOSLER Final Report
Separation of uniform learning classes
Theoretical Computer Science - Special issue: Algorithmic learning theory
Increasing the power of uniform inductive learners
Journal of Computer and System Sciences - Special issue on COLT 2002
Information and Computation
Prescribed Learning of R.E. Classes
ALT '07 Proceedings of the 18th international conference on Algorithmic Learning Theory
Hypothesis Spaces for Learning
LATA '09 Proceedings of the 3rd International Conference on Language and Automata Theory and Applications
Hypothesis spaces for learning
Information and Computation
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This work extends studies of Angluin, Lange and Zeugmann on how learnability of a language class depends on the hypothesis space used by the learner. While previous studies mainly focused on the case where the learner chooses a particular hypothesis space, the goal of this work is to investigate the case where the learner has to cope with all possible hypothesis spaces. In that sense, the present work combines the approach of Angluin, Lange and Zeugmann with the question of how a learner can be synthesized. The investigation for the case of uniformly r.e. classes has been done by Jain, Stephan and Ye [6]. This paper investigates the case for indexed families and gives a special attention to the notions of conservative and non U-shaped learning.