# Learning Geometric Concepts

## Learning Simple Convex Concepts

An algorithm learns a geometric object when it learns a partitioning of space defined by that object. For example, an algorithm learns a polygon when it learns a partitioning of the plane in which the interior of the polygon is labeled with one class and the exterior is labeled with another.

Observation 1. Under the best-case learning model, exactly two examples are required by the NN algorithm to learn a concept by a hyperplane in any number of dimensions.

The strategy of the teacher is to choose any two points equidistant to the line (or hyperplane) such that the line defined by the two points is perpendicular to the original. These two points alone will then correctly classify all future examples. A convex polygon is also very easy to learn in the best case line ( Figure 2 and 2.1).

 Figure 5 Figure 5.1

Learning Complex Geometric Concepts.

Learning complex objects requires some techniques for learning triangles. Recall the definition of a Voronoi diagram: given a set of point S. A point is in the Voronoi diagram of S if it is equidistant to its two NNs (Nearest Neighbors) in S. That is, if a point p has two or more NNs in S, then p is in the Voronoi diagram of S. Therefore, the partitioning of space induced by the NN algorithm is, by definition, a Voronoi diagram.

Observation 2. Suppose we are learning a concept with a triangular boundary. Suppose also that the triangle is not obtuse: no angles are greater than 90 degree. Then exactly nine points are sufficient for the NN algorithm to learn the triangle, using the following strategy.

If l is the length of the shortest side of the triangle, and h is the shortest altitude of the triangle (the minimum distance of any vertex to its opposite side) let =1/2min(l,h). Choose a distance<, and place a point this distance from each corner of the triangle in its interior, such that the point is equidistant from the two adjacent sides. Then reflect each corner point through the two adjacent sides. The resulting voronoi diagram defined by the nine points contains the triangle as a subset. Figure 6 through 7 illustrate such a diagram.

 Figure 6 obtaining the shortest distance among the sides and the heights of this triangle. Figure 6.1 Shortest distance is identified with the red line Figure 6.2 red semi-circle representing the distance  from the corners of the triangle.   Figure 6.3  determining the points from each corners of the triangle and equidistant from the two adjacent sides. Figure 6.4 blue points obtained through the refrection of the red points with respects to the adjacent sides.

 Figure 7 resulting voronoi diagram

The bold lines are the sides of the triangle; the thin lines are the other edges of the Voronoi diagram. The intersections of the Voronoi diagram occur on the vertices, edges, and the interior of the triangle. None of the voronoi diagram occurs outside of the triangle. This observation applies also to d-dimensional objects in which all angles between adjacent faces are less than or equal to 90.

 figure 8  problem with an obtuse triangle

Learning Rectilinear Solids

Theorem1. Any rectilinear polygon with n edges in two dimensions can be learned in the best-case model with nexamples.

Proof. Draw the smallest possible rectangle around the rectilinear polygon. For each edge of the polygon, extend the edge in both directions until it meets the containing rectangle. The resulting grid contain at most n/4 vertices. Since every vertex of the grid has degree 4, we can place four points symmetrically around each vertex( arbitrarily close to the vertex). This set of points will define a Voronoi diagram that contains the grid, and therefore contains the rectilinear polygon. With appropriate labeling of points, the learning is complete, having used n examples. Figure 10 illustrates the grid and the placement of points for the reclitilinear polygon from figure 9.

Figure 9 Example of a rectilinear polygon

Figure 10 Learning a rectilinear polygon