Machine Learning

  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

  A CONCEPT LEARNING TASK •         Consider the example task of learning the target concept " Days on which Tom enjoys his favourite water sport ” Table : Positive and negative training examples for the target concept EnjoySport .   ü   The task is to learn to predict the value of EnjoySport for an arbitrary day , based on the values of its other attributes? ü   What hypothesis representation is provided to the learner?   ·        Let’s consider a simple representation in which each hypothesis consists of a conjunction of constraints on the instance attributes . ·        Let each hypothesis be a vector of six constraints , specifying the values of the six attributes ·        Sky ·        AirTemp ·        Humidity ·        Wind ·        Water ·        Forecast •         For each attribute , the hypothesis will either ü   Indicate by a "?' that any value is acceptable for this attribute , ü   Specify a single required value (e.g., Warm ) for the attribute , or ü   I