CONCEPT LEARNING AS SEARCH
CONCEPT LEARNING AS SEARCH · Concept learning can be viewed as · the task of searching through a large space of hypotheses implicitly defined by the hypothesis representation. · The goal of this search is to · find the hypothesis that best fits the training examples . Example: • Consider the instances X and hypotheses H in the EnjoySport learning task. • The attribute • Sky has three possible values , and • AirTemp , Humidity , Wind, Water, Forecast each have two possible values , • the instance space X contains • exactly 3*2*2*2*2*2 = 96 distinct instances • 5*4*4*4*4*4 = 5120 syntactically distinct hypotheses within H . • Every hypothesis containing one or more "Φ" symbols represents the empty set of instances ; that is, it classifies every instance as negative . • 1 + (4*3*3*3*3*3) = 973 Semantically distinct hypotheses . A CONCEPT LEAR