Geometry-Based Learning Algorithms
George H. John · 2007
We present CHILS , the Convex Hull Inductive Learning System, a novel supervised learning algorithm based on approximating concepts with sets of convex hulls. We introduce a theoretical methodology for describing the power of a concept representation language and use it to compare convex hulls with other geometrical concept representations. The Domain Transform framework (DT) provides a clear way to compare the power of supervised learning systems, allowing us to characterize a class of domains which is learnable by some systems but cannot be learned by other systems. DT can be used similarly to compare the expected generalization performance of different domains. 1 Introduction When performing studies of supervised machine learning algorithms, and when designing new algorithms in particular, it is important to keep in mind the distinction between the learning element and the performance element. Regardless of the intricacies of the learning element, mathematically the performance ele...