Communications of the ACM
Information Processing Letters
Computational limitations on learning from examples
Journal of the ACM (JACM)
A general lower bound on the number of examples needed for learning
Information and Computation
Learnability and the Vapnik-Chervonenkis dimension
Journal of the ACM (JACM)
Prediction-preserving reducibility
Journal of Computer and System Sciences - 3rd Annual Conference on Structure in Complexity Theory, June 14–17, 1988
Qualitative navigation for mobile robots
Artificial Intelligence
Machine learning: a theoretical approach
Machine learning: a theoretical approach
Equivalence of models for polynomial learnability
Information and Computation
Computational learning theory: an introduction
Computational learning theory: an introduction
Efficient noise-tolerant learning from statistical queries
STOC '93 Proceedings of the twenty-fifth annual ACM symposium on Theory of computing
Statistical queries and faulty PAC oracles
COLT '93 Proceedings of the sixth 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
Efficient distribution-free learning of probabilistic concepts
Journal of Computer and System Sciences - Special issue: 31st IEEE conference on foundations of computer science, Oct. 22–24, 1990
Predicting {0, 1}-functions on randomly drawn points
Information and Computation
An introduction to computational learning theory
An introduction to computational learning theory
Bounding the Vapnik-Chervonenkis Dimension of Concept Classes Parameterized by Real Numbers
Machine Learning - Special issue on COLT '93
Image-based navigation through large-scale environments
Image-based navigation through large-scale environments
PAC learning of one-dimensional patterns
Machine Learning
Approximating hyper-rectangles: learning and pseudo-random sets
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Agnostic learning of geometric patterns (extended abstract)
COLT '97 Proceedings of the tenth annual conference on Computational learning theory
Specification and simulation of statistical query algorithms for efficiency and noise tolerance
Journal of Computer and System Sciences - Special issue on the eighth annual workshop on computational learning theory, July 5–8, 1995
On Learning From Multi-Instance Examples: Empirical Evaluation of a Theoretical Approach
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Intrinsic Complexity of Learning Geometrical Concepts from Positive Data
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
Multiple-Instance Learning of Real-Valued Geometric Patterns
Annals of Mathematics and Artificial Intelligence
Intrinsic complexity of learning geometrical concepts from positive data
Journal of Computer and System Sciences
| Hi-index | 0.00 |
Developing the ability to recognize a landmark from a visual image of a robot‘s current location is a fundamental problem in robotics. We describe a way in which the landmark matching problem can be mapped to that of learning a one-dimensional geometric pattern. The first contribution of our work is an efficient noise-tolerant algorithm(designed using the statistical query model) to PAC learn the class ofone-dimensional geometric patterns. The second contribution of ourwork is an empirical study of our algorithm that provides someevidence that statistical query algorithms may be valuable for use inpractice for handling noisy data.