We consider learning models for object recognition from examples.Our method is motivated by systems that use the Hausdor distance as a shape comparison measure.Typically an object is represented in terms of a model shape. A new shape is classified as being an instance of the object when the Hausdor distance between the model and the new shape is s all. We show that such object concepts can be seen as halfspaces (linear threshold functions)in a transformed input space. This makes it possible to use a number of standard algorithms to learn object models fro training examples. When a good model exists, we are guaranteed to find one that provides (with high probability)a recognition rule that is accurate.Our approach provides a easure which generalizes the Hausdor distance in a number of interesting ways.To demonstrate our ethod we trained a system to detect people in images using a single shape model. The learning techniques can be extended to represent objects using multiple model shapes. In this way, we might be able to automatically learn a small set of canonical shapes that characterize the appearance of an object.