Decision theoretic generalizations of the PAC model for neural net and other learning applications
Information and Computation
Robust Parameter Estimation in Computer Vision
SIAM Review
Fitting algebraic curves to noisy data
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Learning Mixtures of Gaussians
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Fitting algebraic curves to noisy data
Journal of Computer and System Sciences - STOC 2002
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In this paper we study the learnability of a mixture of lines model which is of great importance in machine vision, computer graphics, and computer aided design applications. The mixture of lines is a partially-probabilistic model for an image composed of line-segments. Observations are generated by choosing one of the lines at random and picking a point at random from the chosen line. Each point is contaminated with some noise whose distribution is unknown, but which is bounded in magnitude. Our goal is to discover efficiently and rather accurately the line-segments that generated the noisy observations. We describe and analyze an efficient probably approximately correct (PAC) algorithm for solving the problem. Our algorithm combines techniques from planar geometry with simple large deviation tools and is simple to implement.