Agnostic Learning of Geometric Patterns (Extended Abstract)

) Sally A. Goldman Dept. of Computer Science Washington University St. Louis, MO 63130 [email protected] Stephen S. Kwek Dept. of Computer Science Washington University St. Louis, MO 63130 [email protected] Stephen D. Scott Dept. of Computer Science Washington University St. Louis, MO 63130 [email protected] Abstract Goldberg, Goldman, and Scott demonstrated how the problem of recognizing a landmark from a one-dimensional visual image can be mapped to that of learning a one-dimensional geometric pattern and gave a PAC algorithm to learn that class. We present an on-line agnostic learning algorithm for learning the class of one-dimensional geometric patterns. Since, when moving from the processed visual image to a one-dimensional pattern some key information is lost, we define a class of two-dimensional geometric patterns for which the important features from the visual image are incorporated in the geometric pattern, and show how to extend our agnostic learning algorithm to t..

Agnostic Learning of Geometric Patterns (Extended Abstract)

) Sally A. Goldman Dept. of Computer Science Washington University St. Louis, MO 63130 [email protected] Stephen S. Kwek Dept. of Computer Science Washington University St. Louis, MO 63130 [email protected] Stephen D. Scott Dept. of Computer Science Washington University St. Louis, MO 63130 [email protected] Abstract Goldberg, Goldman, and Scott demonstrated how the problem of recognizing a landmark from a one-dimensional visual image can be mapped to that of learning a one-dimensional geometric pattern and gave a PAC algorithm to learn that class. We present an on-line agnostic learning algorithm for learning the class of one-dimensional geometric patterns. Since, when moving from the processed visual image to a one-dimensional pattern some key information is lost, we define a class of two-dimensional geometric patterns for which the important features from the visual image are incorporated in the geometric pattern, and show how to extend our agnostic learning algorithm to t..