Learning decision trees from random examples needed for learning
Information and Computation
Mistake bounds and logarithmic linear-threshold learning algorithms
Mistake bounds and logarithmic linear-threshold learning algorithms
Learning boolean functions in an infinite attribute space
STOC '90 Proceedings of the twenty-second annual ACM symposium on Theory of computing
On-line learning with an oblivious environment and the power of randomization
COLT '91 Proceedings of the fourth annual workshop on Computational learning theory
Rank-r decision trees are a subclass of r-decision lists
Information Processing Letters
Lower Bound Methods and Separation Results for On-Line Learning Models
Machine Learning - Computational learning theory
Learning binary relations and total orders
SIAM Journal on Computing
The weighted majority algorithm
Information and Computation
The Power of Self-Directed Learning
Machine Learning
On-Line Learning of Rectangles and Unions of Rectangles
Machine Learning - Special issue on computational learning theory, COLT'92
Journal of Computer and System Sciences
Reducing the number of queries in self-directed learning
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Being taught can be faster than asking questions
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Machine Learning
Machine Learning
Online learning versus offline learning
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Learning binary relations, total orders, and read-once formulas
Learning binary relations, total orders, and read-once formulas
Measuring teachability using variants of the teaching dimension
Theoretical Computer Science
Recent Developments in Algorithmic Teaching
LATA '09 Proceedings of the 3rd International Conference on Language and Automata Theory and Applications
Recursive teaching dimension, learning complexity, and maximum classes
ALT'10 Proceedings of the 21st international conference on Algorithmic learning theory
Models of Cooperative Teaching and Learning
The Journal of Machine Learning Research
Teaching learners with restricted mind changes
ALT'05 Proceedings of the 16th international conference on Algorithmic Learning Theory
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We study the self-directed (SD) learning model.In this model a learner chooses examples, guesses theirclassification and receives immediate feedback indicatingthe correctness of its guesses.We consider several fundamental questions concerning this model:the parameters of a task that determine the cost oflearning, the computational complexity of a student, andthe relationship between this model and the teacher-directed (TD) learning model.We answer the open problem of relating the cost ofself-directed learning to the VC-dimension by showing that no suchrelation exists. Furthermore, we refute the conjecture that forthe intersection-closed case, the cost of self-directed learning isbounded by the VC-dimension.We also show that the cost ofSD learning may be arbitrarily higher that that of TD learning.Finally, we discuss the number ofqueries needed for learning in this model and itsrelationship to the number of mistakesthe student incurs.We prove a trade-off formula showing that an algorithm that makesfewer queries throughout its learning process, necessarilysuffers a higher number of mistakes.