A computational model of teaching

Summary

A computational analog is presented which allows us to make statements about bounded-complexity teachers and learners, and the model is extended by incorporating trusted information.

Keywords

Dimension (graph theory), Class (philosophy), Computer science, Theoretical computer science, Bounded function

References

Computational Complexity of Learning Read-Once Formulas over Different BasesLisa Hellerstein, Marek Karpiński — 1990
The Knowledge Complexity of Interactive Proof SystemsShafi Goldwasser, Silvio Micali, Charles Rackoff — 1989
Learning Regular Sets from Queries and CounterexamplesDana Angluin — 1987
On the complexity of teachingSally A. Goldman, Michael Kearns — 1991
Inductive inference: an abstract approachJohn C. Cherniavsky, Mahendran Velauthapillai, Richard Statman — 1988
On learning Boolean functionsB. K. Natarajan — 1987
Learning read-once formulas with queriesDana Angluin, Lisa Hellerstein, Marek Karpiński — 1993
Learning binary relations and total ordersSally A. Goldman, Ronald L. Rivest, Robert E. Schapire — 1989
Separating distribution-free and mistake-bound learning models over the Boolean domainAvrim Blum — 1990
A theory of the learnableLeslie G. Valiant — 1984
Identifying μ-formula decision trees with queriesT. Hancock — 1990
Learning read-once formulas over fields and extended basesT. Hancock, L. Hellerstein — 1991

Cited by

Complexity of Teaching by a Restricted Number of ExamplesHayato Kobayashi, Ayumi Shinohara — 2009
Learning from Different TeachersDana Angluin, Mārtiņš Kriķis — 2003
Coaching: learning and using environment and agent models for advicePatrick Riley, Manuela Veloso — 2005
Teaching Dimensions based on Cooperative LearningSandra Zilles, Steffen Lange, Robert C. Holte — 2008
Teaching, learning and explorationYiqun Lisa Yin, Michael Sipser — 1994
Geometric Probing and Testing - A SurveyKathleen Romanik — 1995
Algorithms for learning and teaching sets of vertices in graphsLane Clark, Patricia Evans, Michael R. Fellows — 1994
Learning DFA from Simple ExamplesRajesh Parekh, Vasant Honavar — 1997
On Specifying Boolean Functions by Labelled ExamplesMartin Anthony, Graham Brightwell, John Shawe‐Taylor — 1995
Teaching a smart learnerSally A. Goldman, H. David Mathias — 1993
Teaching a Smarter LearnerSally A. Goldman, H. David Mathias — 1996
Teacher-directed learning in view-independent face recognition with mixture of experts using single-view eigenspacesReza Ebrahimpour, Ehsanollah Kabir, Mohammad Reza Yousefi — 2008
On the complexity of teachingSally A. Goldman, Michael Kearns — 1991
On the limits of efficient teachabilityRocco A. Servedio — 2001
DNF—if you can't learn'em, teach'em: an interactive model of teachingH. David Mathias — 1995
Being taught can be faster than asking questionsRonald L. Rivest, Yiqun Lisa Yin — 1995
Measuring teachability using variants of the teaching dimensionFrank J. Balbach — 2008
Teacher-directed learning in view-independent face recognition with mixture of experts using overlapping eigenspacesReza Ebrahimpour, Ehsanollah Kabir, Mohammad Reza Yousefi — 2008
Massive online teaching to bounded learnersBrendan Juba, Ryan Williams — 2013
Combining labeled and unlabeled data with co-trainingAvrim Blum, Tom M. Mitchell — 1998

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