Two approximations to learning from examples

Two Approximations to Learning from Examples

Learning Best Concept Approximations from Examples

This paper addresses the problem of learning the best approximation of a concept from examples, when the concept cannot be expressed in the learner’s representation language. It presents a method that determines the version space of the best approximations and demonstrates that for any given approximation of the target concept there is a better approximation in this version space. The method do...

A Formal Approach to Learning From Examples

This paper presents a formal, foundational approach to learning from examples in machine learning. It is assumed that a learning system is presented with a stream of facts describing a domain of application. The task of the system is to form and modify hypotheses characterising the relations in the domain, based on this information. Presumably the set of hypotheses that may be so formed wil l r...

Learning functions from examples

This paper concerns algorithms that "learn functions from examples. Functions on strings of a finite alphabet are considered and the notion of dimensionality defined for families of such functions. Using this notion, a theorem is proved identifying the most general conditions under which a family of functions can be efficiently learned from examples. Turning to some familiar families: we presen...

Learning from Order Examples

We advocate a new learning task that deals with orders of items, and we call this the Learning from Order Examples (LOE) task. The aim of the task is to acquire the rule that is used for estimating the proper order of a given unordered item set. The rule is acquired from training examples that are ordered item sets. We present several solution methods for this task, and evaluate the performance...