On the complexity of inductive inference
Information and Control
Theory of recursive functions and effective computability
Theory of recursive functions and effective computability
Learnability and the Vapnik-Chervonenkis dimension
Journal of the ACM (JACM)
Learning unions of boxes with membership and equivalence queries
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
Geometrical concept learning and convex polytopes
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
Learning one-dimensional geometric patterns under one-sided random misclassification noise
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
On-Line Learning of Rectangles and Unions of Rectangles
Machine Learning - Special issue on computational learning theory, COLT'92
Language learning from texts: mindchanges, limited memory, and monotonicity
Information and Computation
On the intrinsic complexity of learning
Information and Computation
Concept learning with geometric hypotheses
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
More or less efficient agnostic learning of convex polygons
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
The intrinsic complexity of language identification
Journal of Computer and System Sciences
PAC learning of one-dimensional patterns
Machine Learning
Noise-tolerant parallel learning of geometric concepts
Information and Computation
Exact Learning of Discretized Geometric Concepts
SIAM Journal on Computing
A Machine-Independent Theory of the Complexity of Recursive Functions
Journal of the ACM (JACM)
The Power of Vacillation in Language Learning
SIAM Journal on Computing
The learnability of unions of two rectangles in the two-dimensional discretized space
Journal of Computer and System Sciences
Agnostic learning of geometric patterns
Journal of Computer and System Sciences
Introduction To Automata Theory, Languages, And Computation
Introduction To Automata Theory, Languages, And Computation
An Introduction to the General Theory of Algorithms
An Introduction to the General Theory of Algorithms
Machine Inductive Inference and Language Identification
Proceedings of the 9th Colloquium on Automata, Languages and Programming
Language Learning From Texts: Degrees of Instrinsic Complexity and Their Characterizations
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
The Intrinsic Complexity of Learning: A Survey
Fundamenta Informaticae
The Intrinsic Complexity of Learning: A Survey
Fundamenta Informaticae
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Intrinsic complexity is used to measure the complexity of learning areas limited by broken-straight lines (called open semi-hulls) and intersections of such areas. Any strategy learning such geometrical concepts can be viewed as a sequence of primitive basic strategies. Thus, the length of such a sequence together with the complexities of the primitive strategies used can be regarded as the complexity of learning the concepts in question. We obtained the best possible lower and upper bounds on learning open semi-hulls, as well as matching upper and lower bounds on the complexity of learning intersections of such areas. Surprisingly, upper bounds in both cases turn out to be much lower than those provided by natural learning strategies. Another surprising result is that learning intersections of open semi-hulls turns out to be easier than learning open semi-hulls themselves.