Communications of the ACM
On the complexity of inductive inference
Information and Control
Probability and plurality for aggregations of learning machines
Information and Computation
Probabilistic inductive inference
Journal of the ACM (JACM)
Trade-off among parameters affecting inductive inference
Information and Computation
COLT '90 Proceedings of the third annual workshop on Computational learning theory
Relations between probabilistic and team one-shot learners (extended abstract)
COLT '91 Proceedings of the fourth annual workshop on Computational learning theory
Breaking the probability ½ barrier in FIN-type learning
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Journal of the ACM (JACM)
Capabilities of probabilistic learners with bounded mind changes
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
The Power of Pluralism for Automatic Program Synthesis
Journal of the ACM (JACM)
Inductive Inference: Theory and Methods
ACM Computing Surveys (CSUR)
An Introduction to the General Theory of Algorithms
An Introduction to the General Theory of Algorithms
Asking Questions Versus Verifiability
AII '92 Proceedings of the International Workshop on Analogical and Inductive Inference
Use of Reduction Arguments in Determining Popperian FIN-Type Learning Capabilities
ALT '93 Proceedings of the 4th International Workshop on Algorithmic Learning Theory
Probabilistic and Pluralistic Learners with Mind Changes
MFCS '92 Proceedings of the 17th International Symposium on Mathematical Foundations of Computer Science
Learning Behaviors of Functions with Teams
Fundamenta Informaticae
| Hi-index | 0.00 |
When learning a concept the learner produces conjectures about the concept he learns. Typically the learner contemplates, performs some experiments, make observations, does some computation, thinks carefully (that is not output a new conjecture for a while) and then makes a conjecture about the (unknown) concept. Any new conjecture of an intelligent learner should be valid for at least some "reasonable amount of time" before some evidence is found that the conjecture is false. Then maybe the learner can further study and explore the concept more and produce a new conjecture that again will be valid for some "reasonable amount of time". In this paper we formalize the idea of reasonable amount of time. The learners who obey the above constraint are called "Thoughtful learners" (TEX learners). We show that there are classes that can be learned using the above model. We also compare this leaning paradigm to other existing ones. The surprising result is that there is no capability intervals in team TEX-type learning. On the other hand, capability intervals exist in all other models. Also these learners are orthogonal to the learners that have been studied in the literature.